The impact of increasing expenditure on National Essential Public Health Services on the medical costs of hypertension in China: A difference-in-difference analysis

Long Xue; Mengyun Sui; YunZhen He; Hongzheng Li; Xiaohua Ying

doi:10.1371/journal.pone.0278026

. 2022 Nov 28;17(11):e0278026. doi: 10.1371/journal.pone.0278026

The impact of increasing expenditure on National Essential Public Health Services on the medical costs of hypertension in China: A difference-in-difference analysis

Long Xue ^1,^*, Mengyun Sui ¹, YunZhen He ¹, Hongzheng Li ¹, Xiaohua Ying ^1,^2,^*

Editor: Jalandhar Pradhan³

PMCID: PMC9704679 PMID: 36441726

Abstract

Background

The prevention and control of hypertension should be an effective way to reduce deaths and it has been a high priority in China. In 2013, the Chinese government increased the subsidy standard for the National Essential Public Health Services Package (NEPHSP) from RMB 15 to RMB 30 per person, which was expected to cover 70 million hypertensions. This study explored the influence of increasing NEPHSP subsidy on outpatient and inpatient expenditure among patients with hypertension.

Methods

Data were mined from the 2011–2015 Harmonized China Health and Retirement Longitudinal Study. The study sample included 3192 hypertensive patients who were not lost to follow-up from 2011 to 2015. Hypertensive patients who covered by NEPHSP from 2011 to 2015 were defined as the treatment group, otherwise defined as the comparison group. The policy intervention was the increase of NEPHSP subsidy in 2013, and the years before and after 2013 were respectively considered as pre- (2011) and post-intervention (2015). The primary outcomes variables were the outpatient and inpatient expenditure of patients with hypertension, based on direct spending of outpatients and inpatients separately reported by patients with hypertension. Using propensity score matching (PSM) to match the individual characteristics of hypertension in the treatment group and the comparison group, difference-in-differences (DID) were used to analyze the outcomes.

Results

The patients with hypertension’ outpatient and inpatient expenditure patterns in the treatment and control group show an increasing trend from 2011 to 2015. After PSM, of the 1 956 hypertensive participants, 369 covered by the NEPHSP before and after 2013. A DID estimate of the increased NEPHSP subsidy was associated with a significant decrease of 1 251.35 RMB (t = 2.13, P = 0.034) in hypertension related inpatient expenditure, no significant change (t = 0.61, P = 0.544) among outpatient expenditure.

Conclusions

The NEPHSP may reduce inpatient expenditure among hypertension. Further strengthening of the NEPHSP may reduce their burden.

Introduction

Two recent studies of hypertension in China suggest that the prevalence of hypertension in China was 23.2% among people aged ≥18 and over from 2012 to 2015 [1], and 44.7% among people aged 35–75 from 2014 to 2017 [2]. Moreover, hypertension increased from 18% [3] to 23.2% [1] from 2002 to 2015. At the same time, several studies using the criteria of the Chinese guidelines for the management of hypertension (systolic BP (SBP) ≥140 mmHg, diastolic BP (DBP) ≥90 mmHg) [4] indicate that less than 50% of patients were awareness, 30–40% were taking antihypertensive medications, and 7.2–15.3% had control [1,2,5]. Using the same nationwide survey but applying the 2017 American College of Cardiology/American Heart Association Guidelines for high blood pressure, hypertension prevalence in China reached 46.4%, while the blood pressure control rate dropped to 3.0% [1].

Cardio-cerebrovascular diseases caused by untreated and inadequately treated hypertension have been the main cause of death among Chinese people [6]. As prevention and control of hypertension should be an effective way to reduce deaths [7], it has been a high priority in China [8]. To address current health challenges, including obesity, hypertension, and non-communicable diseases, and to reduce the disparities for the Chinese population in accessing essential public health services, the Chinese government issued the National Essential Public Health Services Package (NEPHSP) in 2009 [9]; this initiative provides free public health services to meet the challenges posed by hypertension. It includes physical examination, health education, regular health checkups, and regular follow-ups provided to patients with hypertension aged ≥ 35 [10] (see S1 Table for details). At inception, citizens received public health subsidies of RMB 15 from the government, which increased to RMB 30 in 2013, and the government increased support for patients with hypertension from 45 million in 2011 to 70 million in 2013 [11].

Previous studies have indicated that after increasing the subsidy of NEPHSP, patients with hypertension covered by the NEPHSP was associated with an increase of 7.9%, 10.3%, and 10.5% in hypertension the rate of control, medication, and monitoring [9]. Since increasing the subsidy of NEPHSP, the treatment rate and control rate of hypertensive patient increased, so can the outpatient and hospitalization expense of hypertensive patient rise? So far, no relevant research has been conducted. Further, previous studies on the cost of hypertension treatment did not include the effect of the increasing the subsidy of NEPHSP [12–14].

In this study, we used nationally representative longitudinal data to analyze 1) the changes and differences in outpatient and inpatient expenses of hypertensive patients before and after 2013 the increasing the subsidy of NEPHSP and 2) the impact of the increasing the subsidy of NEPHSP on outpatient and hospitalization costs of hypertensive patients.

Materials and methods

Data sources

Data were obtained from the China Health and Retirement Longitudinal Study (CHARLS), a nationally representative longitudinal survey of individuals aged ≥ 45. The national baseline survey was conducted in 2011, with wave 2 in 2013, wave 3 in 2015. It aims to measure the health status, economic status, and well-being of Chinese residents. The survey includes a rich set of questions regarding economic standing, physical and psychological health, demographics, and social networks of aged persons. The population included in the baseline survey was asked the same core questions every two years.

The data distributed across 450 villages in 150 counties [15]. The CHARLS used a multistage probability sampling approach to select a nationally representative sample. Specifically, the first stage involved random sampling, using the probability-proportional-to-size method, that included all county-level units of China with the exception of Tibet, with the final sample comprising 150 countries. The sample was stratified by region and within region by urban or rural status. In the second stage, administrative villages in rural areas and neighborhoods in urban areas were randomly selected as primary sampling units (PSUs). Three PSUs were selected from each county. In the third stage, 24 households were randomly selected based on the geographical locations and lists of each PSU. In the fourth and final stage, a resident aged ≥45 years was randomly selected from a household, and an interview was conducted with both the selected resident and their spouse. For cross-national comparisons of other international aging surveys, a Harmonized CHARLS was created to coordinate the CHARLS with the Health and Retirement Survey in the United States, for which detailed information is publicly available online [16].

Study population

Patients with hypertension were defined according to "A doctor has told you that you have hypertension," and hypertension criteria was based on the 2010 Chinese guidelines for the management of hypertension: systolic BP (SBP) ≥140 mm Hg, diastolic BP (DBP) ≥90 mm Hg [4].

The treatment group was covered by NEPHSP in 2011 and 2015, and the comparison group were not covered by NEPHSP in 2011 and 2015. To determine whether hypertensive patients were covered by NEPHSP, we use the same method as ZHANG et al. [9], using the question “When did you have your last physical examination?” to identify responders who had received physical exams. The question “Who paid for your last physical examination?” measured whether the medical costs were covered by the NEPHSP, given that the NEPHSP includes a free physical exam at least once a year for patients with hypertension. Respondents who chose “government” were the treatment group, and respondents who chose “non-government” had not received a free medical exam and were defined as the comparison group.

Interventions

The policy intervention was the increase of NEPHSP subsidy in 2013, so the year before 2013 (2011) was defined as pre-intervention, and the year after 2013 (2015) was defined as post-intervention.

Outcome measurement

This study reported spending of outpatients and inpatients separately and used the data in in pre-and post-intervention as the dependent variable in the models. All outpatient expenditure in the past month was recorded, including both treatment and medication costs. Inpatient expenditure was defined as all the inpatient costs during the past year, including fees paid to the hospital, ward fees but excluding wages paid to a hired nurse, transportation costs, and accommodation costs for yourself or family members. To identify total hospitalization expenditure in the past year, total medical cost of doctor visits, and amount paid by their insurance company, we used the following items from the CHARLS baseline questionnaire: “How many times have you received inpatient care?” “How many times did you visit a medical facility?” “What is your total hospitalization cost?” and “What is your total outpatient cost?” If the respondent had two or more inpatient or outpatient treatments in the past year or month, then the respondent was asked to list the total medical costs for all visits.

Covariates

The covariate variables for this study consisted of individual socioeconomic characteristics and health information, such as age (45–54, 55–64 and ≥ 65), sex (Men or Women), marital status (Cohabitant or Single), occupation (Farmer or not), education was grouped according to the 1997 International Classification of Standards in Education (1 = Less than lower secondary education, 2 = Upper secondary & vocational training, and 3 = Tertiary education). Household income was classified by four groups according to the 2011 household income quartile: poor (< RMB 2,320), low income (RMB 2,320–11,200), middle income (RMB 11,200–32,400), and high income (≥ RMB 32,400). Other characteristics were health insurance (have or not), Body mass index (BMI) according to the WHO recommendation for the Asian and South Asian population, and is coded as follows: 0 = Normal weight (BMI 18.5–23kg/cm²); 1 = Underweight (BMI < 18.5 kg/cm²), 2 = Overweight or obesity (BMI ≥23 kg/cm²) [17], and self-reported health status (0 = Fair; 1 = Poor; 2 = Good; 3 = Very good or Excellent).

Statistical analysis

As indicated above, respondents covered by NEPHSP in 2011and 2015 were defined as the treatment group, while the comparison group had not received this service. First, the covariates of the two groups of hypertensive patients in 2011 were matched by propensity score matching (PSM) [18], which enables us to calculate weights based on the socioeconomic characteristics and health information of patients with hypertension that yield unbiased estimates of the impact of factors of interest [19]. Patients’ age, sex, marital, work, education, house income, insurance, residence, BMI, health state was matching variables. Logistic regression on each eligible subject was matched according to the propensity score, and because the sample sizes were big difference between the treatment and comparison groups (584 vs. 2 608), using the nearest-neighbor method at a 1:4 ratio with 0.02 in the caliper [20]. The 50 times bootstrap method was employed to obtain the robust standard error. We retained the matched data for the final analysis.

Second, based on the matched data, a difference-in-differences (DID) method was used to analyze the changes in outpatient and inpatient expense for hypertensive patients in pre-and post-intervention. DID can effectively detect the intervention effect between the treatment and comparison groups and isolate the time trend unrelated to the intervention [21]. The effect of the NEPHSP was estimated by comparing the differences between two changes in outcomes: (1) changes between pre- and post- intervention within the treatment group and (2) the pre-and post-intervention periods in the comparison group [22]. This estimate was computed from a regress model, which includes two dichotomous variables: the time (pre- or post- intervention), the group (treatment or comparison group) and an interaction term between the time and the group. The impact was estimated through the coefficient of this interaction term. The model allows adjustment for potential confounders (age, sex, marital, work, education, house income, insurance, residence, BMI, health state) to reduce residual confounders [23]. Given that the skewed distribution of outpatient and inpatient expense violated the normal distribution assumption of the regress model, logarithmic transformation was adopted in the analysis [24].

Data analyses were performed using Stata v.16.0 (StataCorp, Texas, USA). Chi-square test was used to test the difference between the categorical data. A P-value <0.05 was considered statistically significant.

Ethics approval and informed consent statement

Ethical approval for collecting data on human subjects was obtained from the Biomedical Ethics Review Committee of Peking University (IRB00001052-11015). All participants provided written informed consent. All participants provided written informed consent.

Results

Sample characteristics

Before PSM, a total of 3 192 hypertensive patients who were not lost to follow-up were included in the study, including 584 patients in the treatment group and 2608 patients in the comparison group. After matching, there were 369 cases in the treatment group and 1587 cases in the comparison group (Fig 1).

Table 1 presents the socioeconomic and health characteristics (N = 3,192) in 2011. Compared with treatment group, the age (χ2 = 24.50, P<0.001) and health status (χ2 = 8.63, P = 0.035) in the comparison group was significantly different. There were no significant differences in sex, marital, education, household income, health insurance, residence, and BMI between the two groups.

Table 1. Baseline characteristics of patients with hypertension (% 95%CI).

	Comparison (%, 95%CI)			Treatment (%, 95%CI)			χ2	p
N	2,608			584
Age
45–54	19.70	16.11	23.87	28.18	26.64	29.78	32.32	<0.001
55–64	37.68	33.09	42.52	40.56	38.86	42.29
≥65	42.61	37.87	47.49	31.25	29.66	32.89
Sex
Men	46.32	41.52	51.20	43.86	42.14	45.59	1.19	0.276
Women	53.68	48.80	58.48	56.14	54.41	57.86
Marital
Cohabitant	80.88	76.76	84.42	80.58	79.17	81.92	0.01	0.905
Single	19.12	15.58	23.24	19.42	18.08	20.83
Work
Not farmer	25.25	21.26	29.70	23.33	21.89	24.84	1.00	0.317
Farmer	74.75	70.30	78.74	76.67	75.16	78.11
Education
Less than lower secondary education	91.91	88.83	94.20	89.55	88.43	90.57	4.19	0.123
Upper secondary & vocational training	7.11	4.98	10.05	8.81	7.87	9.85
Tertiary education	0.98	0.37	2.59	1.64	1.25	2.15
Household income
Poor	25.53	20.76	30.97	25.04	23.23	26.93	3.73	0.292
Low income	24.47	19.78	29.85	24.99	23.19	26.88
Middle income	22.34	17.84	27.60	25.41	23.60	27.32
High income	27.66	22.73	33.20	24.56	22.77	26.45
Health insurance
No	5.64	3.77	8.35	6.64	5.82	7.56	0.94	0.331
Yes	94.36	91.65	96.23	93.36	92.44	94.18
Residence
Urban	40.20	35.53	45.05	39.60	37.91	41.31	0.08	0.779
Rural	59.80	54.95	64.47	60.40	58.69	62.09
BMI (kg/cm2)
Normal weight (18.5–23)	30.86	26.14	36.02	28.09	26.37	29.87	2.38	0.305
Underweight (<18.5)	4.15	2.47	6.90	3.63	2.97	4.43
Overweight or obesity (≥23)	64.99	59.72	69.91	68.28	66.44	70.07
Self-reported health status
Fair	50.49	44.88	56.10	46.63	44.58	48.69	8.63	0.035
Poor	29.84	24.95	35.23	35.21	33.27	37.20
Good	12.46	9.19	16.68	12.91	11.59	14.36
Very good or Excellent	7.21	4.79	10.73	5.24	4.40	6.24

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BMI: Body mass index.

Baseline characteristics after PSM

Table 2 illustrates the socioeconomic and health characteristics before and after the PSM in 2011. Before matching, the treatment group (n = 2 608) and comparison group (n = 548) differed significantly in terms of age ≥60 (t = 4.61, p< 0.001) and self-reported poor health (t = -2.26, p = 0.025). After matching, the differences in the socioeconomic and health characteristics between the two groups were insignificant. 215 participants in the treatment group were lost after matching, which may indicate selection bias. For this reason, we compared the matched sample with the original sample, and no significant differences were found between the two groups, as shown in S2 Table.

Table 2. Comparable characteristics between the two groups after PSM.

	Before matching				After matching
	Comparison	Treatment	t	p	Comparison	Treatment	t	p
N	2608	584			1587	369
Age
45–54	Ref.
55–64	37.69	40.56	-1.11	0.266	31.71	34.15	-0.47	0.640
≥65	42.61	31.25	4.61	0.000	54.27	52.74	0.28	0.783
Sex
Men	Ref.
Women	53.68	56.14	-0.94	0.345	42.68	38.57	0.76	0.449
Marital
Cohabitant	Ref.
Single	19.12	19.42	-0.14	0.885	21.95	16.62	1.22	0.222
Work
Not farmer	Ref.
Farmer	74.76	76.67	-0.86	0.392	78.05	78.35	-0.07	0.947
Education
Less than lower secondary education	Ref.
Upper secondary & vocational training	7.11	8.81	-1.15	0.249	6.10	4.57	0.61	0.541
Tertiary education	0.98	1.64	-1.01	0.311	0.61	1.22	-0.58	0.563
Household income
Poor	Ref.
Low income	24.47	24.99	-0.19	0.850	31.10	31.10	0.00	1.000
Middle income	22.34	25.42	-1.12	0.263	18.29	18.14	0.04	0.972
High income	27.66	24.56	1.13	0.259	24.39	23.78	0.13	0.898
Health insurance
No	Ref.
Yes	94.36	93.36	0.77	0.441	95.73	96.80	-0.51	0.612
Residence
Urban	Ref.
Rural	59.80	40.56	-1.11	0.266	67.68	72.26	-0.90	0.368
BMI (kg/cm2)
Normal weight (18.5–23)	Ref.
Underweight (<18.5)	4.15	3.63	0.48	0.631	6.71	4.42	0.90	0.368
Overweight or obesity (≥23)	64.99	68.28	-1.22	0.223	59.76	64.33	-0.85	0.395
Self-reported health status
Fair	Ref.
Poor	29.84	35.21	-2.26	0.025	32.32	30.03	0.45	0.656
Good	12.46	14.91	-1.85	0.064	12.81	10.37	0.69	0.492
Very good or Excellent	7.21	5.25	1.42	0.156	3.05	2.44	0.34	0.736

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BMI: Body mass index.

S1–S4 Figs illustrate the standardized bias across the covariates in the -10% to 10% range, propensity score distribution, and kernel density across the two groups’ propensity scores before and after matching, and the matching effect was satisfactory.

Outpatient and inpatient expenditure changes pre- and post-NEPHSP

Figs 2 and 3 presents before and after PSM, the patients with hypertension’ outpatient and inpatient expenditure patterns in the treatment group and the control group show an increasing trend from 2011 to 2015. After PSM, there was a difference in hospitalization costs between the treatment group and the control group (2888.91 VS. 1837.49, t = 1.73, P = 0.084) in 2015.

Common trend assumption

Fig 4 illustrates the expenditure of outpatients and inpatients, compared between the comparison and treatment groups in pre-and post-intervention, respectively, after PSM. No statistically significant differences for the outpatient (p = 0.473) and inpatient expenses (p = 0.730) in the pre-intervention period estimates were observed.

Comparing the treatment group and the comparison group

Table 3 presents the results from the DID analyses of outpatient and inpatient expenditure changes between treatment and comparison groups after PSM. Compared with pre-intervention, the treatment group was associated with a decrease of 266.23 RMB in outpatient expenditure post-intervention, but there was no statistically significant (t = 0.61, P = 0.544). In terms of inpatient expenditure, the treatment group was associated with a significant decrease of 1251.35 RMB (t = 2.13, P = 0.034) in inpatient expenditure post-intervention compared with pre-intervention.

Table 3. Impact of the NEPHSP on the outpatient and inpatient expenditure after PSM.

	Comparison ( $\bar{x}$ (sd))	Treatment ( $\bar{x}$ (sd))	Marginal difference
Inpatient
pre-intervention	979.61(-5840.3)	1179.54(-5247.45)	199.93(590.23)
post-intervention	2888.91(-14059.57)	1837.49(-12068.14)	-1051.42*(607.67)
DID estimates			-1251.35**(859.37)
Outpatient
pre-intervention	204.13(-1411.47)	84.89(-426.71)	-119.24(166.41)
post-intervention	408.74(-2726.68)	555.73(-6030.02)	146.99(117.67)
DID estimates			266.23(203.81)

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*p<0.10

**p<0.05.

Discussion

Our results revealed that the inpatient expenditure of patients with hypertension decreased after NEPHSP’s subsidy was increased. Previous research indicates that increasing NEPHSP’s subsidy improved the rate of hypertension control, antihypertensive medication use, and blood pressure monitoring [9]. Other research has indicated that providing free public health services to patients with hypertension can effectively reduce their burden [25]. The NEPHSP may have a positive influence that is related to improvements in hypertension diagnosis, treatment, and control. Indeed, some signs point to a shift toward reactive care for prevention [26], which may be positively correlated with promoting healthy lifestyle traits, such as a low sodium diet [27], physical activity, weight loss, and smoking and drinking cessation [28]. It may also signal decreases in hospitalization rates, lengths of stay, or procedures for complications [29]. The NEPHSP provides free preventive care services for patients with hypertension to prevent or delay comorbidity and complications of the disease and lower overall healthcare costs. These results are similar to those of a study conducted in Japan that demonstrated that free screening and treatment services for patients with hypertension were associated with a 42–75% reduction in stroke incidence [30]. Thus, it is not surprising that in China, the expanded NEPHSP can reduce the disease burden of patients with hypertension.

There was an overall upward trend in expenditure associated with hypertension from 2011 to 2015, which might be related to the increasing incidence of hypertension in China. China is one of the fastest growing countries all over the world [31], the prevalence of self-reported hypertension among people aged 15 and over rose from 6.7% in 2008 to 14.2% in 2013. Meanwhile, the number of patients with hypertension older than 65 increased from 21.6% to 37.0% and the number of patients with hypertension aged 45 to 64 also doubled during this period [32]. The life expectancy of patients with hypertension and incidence of young hypertensive patients increased. Correspondingly, healthcare expenditure increased, as people with hypertension live longer and their chances of developing comorbidities and complications increase [28,33]. One study in China revealed that 87% of heart disease deaths, 71% of stroke deaths, 54% of ischemic heart disease deaths, 41% of other cardiovascular disease deaths, and 43% of chronic kidney disease deaths were attributable to hypertension [34], which may also be one of the reasons for the increased cost for patients with hypertension observed in this study.

After 2013, Those covered by the NEPHSP displayed an upward tendency in outpatient services and a downward tendency in hospitalization services. These findings suggest that the expand of the NEPHSP might be achieving its goals of improving hypertension management. The same results have been confirmed in other studies in China [9,35]. Compared with 2002, the number of patients who were aware they had hypertension increased by 54 million, the number who were treated increased by 53 million, and the number whose blood pressure was under control increased by 0.25 million. For people covered by NEPHSP, higher awareness rates lead to more outpatient visits and fewer complications lead to lower hospitalization costs. This is largely because of the government’s continued efforts to reform the healthcare system and equalize public health services.

We noted that healthcare costs for patients with high blood pressure increased more slowly in the comparison groups after 2013. This may be due to “spillover effects” from the NEPHSP. The same spillover effects have been observed in other policy studies [36,37]. Our results suggest that there might be a positive spillover effect in comparison group.

We acknowledge that there are several limitations to this study. First, although PSM was used to eliminate some individual selection bias factors, there are numerous confounding factors, such as the living environment, social network, access to health care in the place of residence, medical consumer price level, and healthcare supply in the place of residence. Second, our dependent variable fails to isolate the costs purely because of hypertension, and other comorbidities of the patients with hypertension may interfere with the results. Future studies must link the survey data and the medical treatment data to obtain the pure cost of hypertension. Moreover, there was a considerable amount of zero values in the dependent variable, which may affect the result estimates, as we cannot determine whether the cost was due to patient visits or non-visits to healthcare providers. Third, outpatient and inpatient costs were based on respondents’ recollections and may have been missed and overreported, adversely affecting our estimates. In the future, we will use the medical bills to increase the accuracy of the cost information. Fourth, Indirect measurement of intervention in this study would affect the results of the article, and we need to find cleaner interference indicators in the future. Finally, we used data for patients with hypertension aged 45 and over in the CHARLS, but patients with hypertension aged 35 and over were covered by the NEPHSP, which may have influenced our estimates. In the future, we will use more nationally representative samples and include more variables to study this issue.

Conclusion

In China, the expand of the NEPHSP may have reduced outpatient and inpatient treatment expense for patients with hypertension, although outpatient costs were not significant. The NEPHSP is an important health policy that establishes a file for hypertensive patients, regular check-ups and encourages patients to seek medical care reasonably and regularly. In the future, financial compensation should be increased, the catalog of free screening services for chronic diseases should be expanded, the occurrence of chronic disease complications should be avoided or delayed, and the burden of chronic diseases should be reduced. For policymakers, the NEPHSP may have begun to achieve its goals of increasing the health awareness of patients with hypertension and reducing their financial burden.

Supporting information

S1 Fig. Test of standardized percentage bias across covariates before and after propensity score matching.

Illustrates the standardized bias across the covariates in the -10% to 10% range across the two groups’ propensity scores before and after matching, and the matching effect was satisfactory.

(TIF)

Click here for additional data file.^{(109.3KB, tif)}

S2 Fig. Test of propensity score distribution before and after propensity score matching.

Illustrates the propensity score distribution across the two groups’ propensity scores before and after matching, and the matching effect was satisfactory.

(TIF)

Click here for additional data file.^{(65KB, tif)}

S3 Fig. Test of propensity score kernel density before propensity score matching.

Illustrates the propensity kernel density across the two groups’ propensity scores before matching.

(TIF)

Click here for additional data file.^{(82.9KB, tif)}

S4 Fig. Test of propensity score kernel density after propensity score matching.

Illustrates the propensity kernel density across the two groups’ propensity scores after matching, and the matching effect was satisfactory.

(TIF)

Click here for additional data file.^{(80.1KB, tif)}

S1 Table. 2009 National Essential Public Health Services Package (NEPHSP) content.

Description of data: The Chinese government issued the National Essential Public Health Services Package (NEPHSP) in 2009; this initiative provides free public health services, including health education, regular health checkups, and regular follow-ups.

(DOCX)

Click here for additional data file.^{(22.3KB, docx)}

S2 Table. Comparison of samples before and after PSM.

Description of data: Comparison of samples data before and after matching, and the groups no significant difference.

(DOCX)

Click here for additional data file.^{(27.4KB, docx)}

Acknowledgments

We thank the China Health and Retirement Longitudinal Study (CHARLS) team for providing data.

Data Availability

All the data we used have been publicly released on the CHARLS website: https://charls.charlsdata.com/pages/data/111/zh-cn.html.

Funding Statement

The authors received no specific funding for this work.

References

1.Wang Z, Chen Z, Zhang L, Wang X, Hao G, Zhang Z, et al. Status of Hypertension in China: Results From the China Hypertension Survey, 2012–2015. Circulation 2018;137:2344–56. doi: 10.1161/CIRCULATIONAHA.117.032380 [DOI] [PubMed] [Google Scholar]
2.Lu J, Lu Y, Wang X, Li X, Linderman GC, Wu C, et al. Prevalence, awareness, treatment, and control of hypertension in China: data from 1·7 million adults in a population-based screening study (China PEACE Million Persons Project). Lancet (London, England) 2017;390:2549–58. 10.1016/S0140-6736(17)32478-9. [DOI] [PubMed] [Google Scholar]
3.Wu Y, Huxley R, Li L, Anna V, Xie G, Yao C, et al. Prevalence, awareness, treatment, and control of hypertension in China: data from the China National Nutrition and Health Survey 2002. Circulation 2008;118:2679–86. doi: 10.1161/CIRCULATIONAHA.108.788166 [DOI] [PubMed] [Google Scholar]
4.Liu LS, Writing Group of 2010 Chinese Guidelines for the Management of Hypertension. [2010 Chinese guidelines for the management of hypertension]. Zhonghua Xin Xue Guan Bing Za Zhi 2011;39:579–615. 10.1038/ajh.2011.248a. [DOI] [PubMed] [Google Scholar]
5.Li W, Gu H, Teo KK, Bo J, Wang Y, Yang J, et al. Hypertension prevalence, awareness, treatment, and control in 115 rural and urban communities involving 47 000 people from China. J Hypertens 2016;34:39–46. doi: 10.1097/HJH.0000000000000745 [DOI] [PubMed] [Google Scholar]
6.Kyu HH, Bachman VF, Alexander LT, Mumford JE, Afshin A, Estep K, et al. Mortality, morbidity, and risk factors in China and its provinces, 1990–2017: a systematic analysis for the Global Burden of Disease Study 2017. Lancet 2019;394:1145–58. doi: 10.1016/S0140-6736(19)30427-1 [DOI] [PMC free article] [PubMed] [Google Scholar]
7.MSc DE, HBSc CAE, PhD AK, PhD SGA, MB TC, PhD JE, et al. Blood pressure lowering for prevention of cardiovascular disease and death: a systematic review and meta-analysis. Lancet 2015:1–11. [DOI] [PubMed] [Google Scholar]
8.Wang L, Kong L, Wu F, Bai Y, Burton R. Preventing chronic diseases in China. Lancet 2005;366:1821–4. doi: 10.1016/S0140-6736(05)67344-8 [DOI] [PubMed] [Google Scholar]
9.Zhang D, Pan X, Li S, Liang D, Hou Z, Li Y, et al. Impact of the National Essential Public Health Services Policy on Hypertension Control in China. Am J Hypertens 2018;31:115–23. 10.1093/ajh/hpx139. [DOI] [PubMed] [Google Scholar]
10.Ministry of Health. PRC (2009) Opinions on promoting the essential public health services gradually equalization. 2009. http://www.nhc.gov.cn/jws/s3581r/200910/fe1cdd87dcfa4622abca696c712d77e8.shtml?from=singlemessage (accessed October 27, 2020).
11.Ministry of Health. PRC (2015) Notice on inform national essential public health services in 2015. 2015. http://www.nhc.gov.cn/jws/s3577/201506/61340494c00e4ae4bca0ad8411a724a9.shtml (accessed October 27, 2020).
12.Pan F, Wang H, Wang Z, Su C, Zhang J, Zhang B. Medical cost attributable to hypertension among Chinese middle and old aged residents in 2011. Wei Sheng Yan Jiu 2017;46:246–50. [PubMed] [Google Scholar]
13.Bao-rong YF. Analysis on annual change of medical expenses for hypertensive inpatients with different types of health insurance. Chinese Heal Resour 2012;15:406–8. [Google Scholar]
14.XlONG Ju-yang, CHEN Ke-jan, XIONG Xian-jun et al.. Analysis of the Cost and Influence Factors Urban Hypertensive Patients in China. Chinese Heal Econ 2010;29:81–3. [Google Scholar]
15.Y. Z, Y. H, J.P. S, J. S, G. Y. Cohort profile: The China health and retirement longitudinal study (CHARLS). Int J Epidemiol 2014;43:61–8. 10.1093/ije/dys203 LK - http://resolver.ebscohost.com/openurl?sid=EMBASE&issn=03005771&id=doi:10.1093%2Fije%2Fdys203&atitle=Cohort+profile%3A+The+China+health+and+retirement+longitudinal+study+%28CHARLS%29&stitle=Int.+J.+Epidemiol.&title=International+Journal+of+Epidemiology&volume=43&issue=1&spage=61&epage=68&aulast=Zhao&aufirst=Yaohui&auinit=Y.&aufull=Zhao+Y.&coden=IJEPB&isbn=&pages=61-68&date=2014&auinit1=Y&auinitm=. [DOI] [PMC free article] [PubMed] [Google Scholar]
16.Beaumaster S, Chien S, Lau S, Lin A, Phillps D, Wilkens J, et al. Harmonized CHARLS Documentation 2018;Version C:136. [Google Scholar]
17.Barba C, Cavalli-Sforza T, Cutter J, Darnton-Hill I, Deurenberg P, Gill T, et al. Appropriate body-mass index for Asian populations and its implications for policy and intervention strategies. Lancet 2004;363:157–63. doi: 10.1016/S0140-6736(03)15268-3 [DOI] [PubMed] [Google Scholar]
18.Caliendo M, Kopeinig S. Some practical guidance for the implementation of propensity score matching. J Econ Surv 2008;22:31–72. 10.1111/j.1467-6419.2007.00527.x. [DOI] [Google Scholar]
19.Rosenbaum PR, Rubin DB. The Central Role of the Propensity Score in Observational Studies for Causal Effects. Biometrika 1983;70:41. 10.2307/2335942. [DOI] [Google Scholar]
20.Xian W, Xu X, Li J, Sun J, Fu H, Wu S, et al. Health care inequality under different medical insurance schemes in a socioeconomically underdeveloped region of China: A propensity score matching analysis. BMC Public Health 2019;19. 10.1186/s12889-019-7761-6. [DOI] [PMC free article] [PubMed] [Google Scholar]
21.Karan A, Yip W, Mahal A. Extending health insurance to the poor in India: An impact evaluation of Rashtriya Swasthya Bima Yojana on out of pocket spending for healthcare. Soc Sci Med 2017;181:83–92. doi: 10.1016/j.socscimed.2017.03.053 [DOI] [PMC free article] [PubMed] [Google Scholar]
22.Godard-Sebillotte C, Karunananthan S, Vedel I. Difference-in-differences analysis and the propensity score to estimate the impact of non-randomized primary care interventions. Fam Pract 2018;36:247–51. 10.1093/fampra/cmz003. [DOI] [PMC free article] [PubMed] [Google Scholar]
23.Dimick JB, Ryan AM. Methods for evaluating changes in health care policy: The difference-in-differences approach. JAMA—J Am Med Assoc 2014;312:2401–2. doi: 10.1001/jama.2014.16153 [DOI] [PubMed] [Google Scholar]
24.Axon DR, Chien J. Assessing healthcare expenditures of older United States adults with pain and poor versus good mental health status: A cross-sectional study. BMJ Open 2022;12. doi: 10.1136/bmjopen-2021-049727 [DOI] [PMC free article] [PubMed] [Google Scholar]
25.Song Z, Hill C, Bennet J, Vavasis A, Oriol NE. Mobile clinic in massachusetts associated with cost savings from lowering blood pressure and emergency department use. Health Aff 2013;32:36–44. doi: 10.1377/hlthaff.2011.1392 [DOI] [PMC free article] [PubMed] [Google Scholar]
26.Kirkland EB, Heincelman M, Bishu KG, Schumann SO, Schreiner A, Axon RN, et al. Trends in healthcare expenditures among US adults with hypertension: National estimates, 2003–2014. J Am Heart Assoc 2018;7:e008731. doi: 10.1161/JAHA.118.008731 [DOI] [PMC free article] [PubMed] [Google Scholar]
27.Li X, Jan S, Yan LL, Hayes A, Chu Y, Wang H, et al. Cost and cost-effectiveness of a school-based education program to reduce salt intake in children and their families in China. PLoS One 2017;12. doi: 10.1371/journal.pone.0183033 [DOI] [PMC free article] [PubMed] [Google Scholar]
28.Chinese Preventive Medicine Association; Chinese Preventive Medical Association Heart Disease Prevention and Control Professional Committee; Chinese Medical Association Diabetes Society; Stroke Prevention and Control Professional Committee of Chinese Prev. Chinese guidelines for healthy lifestyle prevention of cardiovascular and metabolic diseases. Chi J Prev Med 2020;03:256–77. [Google Scholar]
29.Yan-qing Liu, Ya-na Bai, Hai-yan Li et al. Study on trends of disease burden of hypertension in Jinchang Cohort. J Lanzhou Univ Sci 2015;41:2015. 10.13885/j.issn.1000-2812.2015.06.007. [DOI] [Google Scholar]
30.Iso H, Shimamoto T, Naito Y, Sato S, Kitamura A, Iida M, et al. Effects of a long-term hypertension control program on stroke incidence and prevalence in a rural community in Northeastern Japan. Stroke 1998;29:1510–8. doi: 10.1161/01.str.29.8.1510 [DOI] [PubMed] [Google Scholar]
31.Dong X, Rao J, Ye Y, Pang M, Yang J, Wang H, et al. Trends and associated factors of hypertension among residents aged ≥15 years in Guangzhou, China, 2004–2013. Iran J Public Health 2018;47:198–208. [PMC free article] [PubMed] [Google Scholar]
32.Center for Health Statistics and Information. China Ministry of Health. An Analysis Report of National Health Services Survey in China, 2013 2013. [Google Scholar]
33.Dong yunqiu Qian Yun, Xuanxuan Wang et al. Economic Burden of Patients with Hypertension or Type 2 Diabetes Mellitus in Wuxi’ Communities. Chinese Heal Serv Manag 2015;32:18–21. [Google Scholar]
34.Mingbo Liu, Yichong Li, Shiwei Liu et al. Burden on blood-pressure-related diseases among the Chinese population,in 2010. Chinese J Epidemiol 2014;6:680–3. 10.3760/cma.j.issn.0254-6450.2014.06.014. [DOI] [PubMed] [Google Scholar]
35.Fang W, Yong-Bin L, Xue D TD. the National Essential Public Health Services project in China: Progress and equity. Chin J Heal Pol 2013;5:9–14. [Google Scholar]
36.Shane DM, Ayyagari P. Spillover effects of the affordable care act? Exploring the impact on young adult dental insurance coverage. Health Serv Res 2015;50:1109–24. doi: 10.1111/1475-6773.12266 [DOI] [PMC free article] [PubMed] [Google Scholar]
37.Einav L, Finkelstein A, Ji Y, Mahoney N. Randomized trial shows healthcare payment reform has equal-sized spillover effects on patients not targeted by reform. Proc Natl Acad Sci U S A 2020;117:18939–47. doi: 10.1073/pnas.2004759117 [DOI] [PMC free article] [PubMed] [Google Scholar]

PLoS One. doi: 10.1371/journal.pone.0278026.r001

Decision Letter 0

Jalandhar Pradhan

20 Sep 2022

PONE-D-22-22961The impact of increasing expenditure on National Essential Public Health Services on the medical costs of hypertension in China: A difference-in-difference analysisPLOS ONE

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Reviewer #2: Yes

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Reviewer #1: No

Reviewer #2: Yes

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Reviewer #1: Yes

Reviewer #2: Yes

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5. Review Comments to the Author

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Reviewer #1: Overall, the writing of this manuscript did not meet the criteria of a scientific paper. The introduction did not describe the significance of this study clearly. The methods are difficult to follow. The intervention was not measured directly. The results did not show the major findings quantitatively.

Abstract:

- The methods should describe the study sample selection and intervention and define primary outcomes.

- The first sentence in the results repeated the last sentence in the methods. Additionally, the results should describe the major findings quantitatively. Using "significant decrease" alone is not sufficient.

Introduction:

- The purpose of this is to examine the effect of NEPHSP on health care costs in patients with hypertension. What is the background of initiating NEPHSP? What are the barriers to hypertension treatment in China?

- Line 28: What is the time period of incidence?

- Lines 36-37: How do Chinese clinical guidelines define hypertension and treatment goals?

- The introduction (lines 51-52) identified some gaps in the literature. Did your study address these gaps? Based on the methods and results, your study did not assess medication use, blood pressure monitoring, or hypertension-related costs. It seems that the introduction did not reflect the significance of your study.

- Line 50: Using "positively associated with..." is too general. Please describe the findings quantitatively.

Methods:

- The description of the data source is not clear. What do you mean the survey was administered every two years since 2011? Do you mean the survey was started in 2011 and followed the participants till 2015? Was the survey disseminated every two years to the same participants, i.e., 2011, 2013, and 2015? Did the survey ask the same questions during each cycle?

- What information was collected by the survey?

- What sampling method was used in this survey? Did this survey use a complex survey design?

- What is the purpose of this survey?

- What are the inclusion and exclusion criteria of the study sample?

- Was self-reported hypertension from the diagnosis of hypertension told by doctors or participants' perceptions?

- Line 71: what do you mean all included participants were not covered by NEPHSP?

- Line 77: How to define outpatient and inpatient spending?

- Lines 79 – 80: Is it difficult to understand "Direct medical costs were included in inpatient expenditure?" What is the definition of medical cost in this study? Usually, medical costs include inpatient and outpatient expenses.

- Line 87: How could patients report the expenses accurately, including the amount paid by insurance and out-of-pocket payment? If the cost information just relied on participants' memory, this would be a limitation.

- Lines 94-97: It seems that you made an assumption to define the intervention indirectly based on the question, "Who paid for your last physical examination?" If so, is it possible that some participants could lose coverage paid by the government during the follow-up period? Why were some participants covered by government insurance and some not? Additionally, lines 70-71 mentioned all participants were not covered by NEPHSP. How to explain the inconsistency? The indirect measurement of intervention to define treatment and control groups is a big concern.

- Line 102: The survey population represents people >=45 years. Why did you use >=65 or not to define age? Shouldn't it be 45-64 and 65+?

- Line 104: The education level is confusing. Do you mean Middle school represents the highest education level?

- Line 109: Usually, the health status is categorized into poor-fair, good, very good-excellent. Did the survey use poor and not poor to define the health status? If so, the category of not poor is too broad and could not show the true association between health status and outcome.

- Line 113: What gap? Do you mean the characteristics of the two groups were significantly different?

- Lines 114-115: How did you estimate propensity score? Did you use logistic regression? What covariates were included? If you used 1:4 matching, there should be a big difference in the sample size between the two groups. What is the original sample size for each group?

- Lines 125-126: How to define pre- and post- NEPHSP within treatment group? All people in the treatment had been covered by NEPHSP since 2011. Where to get the data before 2011?

- The outcome measures are not clear. The detailed difference-in-difference in costs should be elaborated. In the linear regression model, what is the dependent variable?

Results:

- Before propensity score matching, what is the original sample size for each group? When the propensity score matching was applied, did you include all participants in the treatment for matching or lose some participants? According to Figure 1, you lost 215 participants in the treatment group after matching. The concern is whether the matched sample can still represent the original study sample. Is there a selection bias in the study design?

- Table 1: p values are needed to show the significance of all the variables between the two groups. Usually, the first-year characteristics reflect the baseline characteristics. Why did you list characteristics in 2015?

- The results should describe the major findings in Tables ad figures quantitatively. Using "significant difference" is too general.

- Did you compare the results in Table 3 before and after PSM?

Conclusion:

- The conclusion should be based on the results, and the conclusion paragraph seems to be about policy implications.

Reviewer #2: This paper used data from the 2011-2015 Harmonized China Health and Retirement Longitudinal Study to assess the impact of China’s National Essential Public Health Services Package (NEPHSP) on hypertension services utilization and expenditure. The authors used difference-in-differences with propensity score matching to control unobserved heterogeneities. The paper is well-written and with rigor. I have some minor comments for the authors to consider.

First, the manuscript needs a careful copyediting with occasional misuses of English. I will list some specific examples later.

Second, while the methods appear to be well-executed, some clarifications are useful. For instance, why “1:4 matching” is used? What variables are used in defining “neighbor”? Expenditures tend to be skewed, is there any effort to address it with specification tests and generalized linear models?

Specific Comments

Line 140: Table 1: A t-test comparing the “treatment” and the “control” group would be useful.

Line 149: Table 2, “Not poor self-reported health” should be rephrased, maybe “self-reported good or better health”

Line 22-23: it should be “in hypertension related inpatient expenditure”

Line 169-173: need a concise title for Figure 4 – could put some of the information in a footnote.

Line 178: The authors occasionally used “compared to” – note that “compare to” highlights similarities while “compare with” stresses on the differences.

Line 102-108: Sex should be (Men or women), “living with or without a partner” could be cohabitant – might need to be clarified or recoded. Education categories could be cleaned up as well. The income thresholds need to have a brief justification – it will be confusing to readers why those are used. “insurance” – should be health insurance. By the way, what are the insurance?

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Reviewer #1: No

Reviewer #2: No

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PLoS One. 2022 Nov 28;17(11):e0278026. doi: 10.1371/journal.pone.0278026.r002

Author response to Decision Letter 0

11 Oct 2022

When submitting your revision, we need you to address these additional requirements.

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https://journals.plos.org/plosone/s/file?id=ba62/PLOSOne_formatting_sample_title_authors_affiliations.pdf

The author made a comprehensive format modification according to the requirements of the magazine

2. Thank you for stating in your Funding Statement:

"NO - Include this sentence at the end of your statement: The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript"

Please include your amended Funding Statement within your cover letter. We will change the online submission form on your behalf.

The authors have added this sentence at the fund of statement for the article that the funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript, and the same change have been made in cover letter.

3. Thank you for stating the following in the Acknowledgments Section of your manuscript:

"We also thank all members of the NHC Key Laboratory of Health Technology Assessment for providing assistance."

Please remove any funding-related text from the manuscript and let us know how you would like to update your Funding Statement. Currently, your Funding Statement reads as follows:

"NO - Include this sentence at the end of your statement: The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript"

Please include your amended statements within your cover letter; we will change the online submission form on your behalf.

"We also thank all members of the NHC Key Laboratory of Health Technology Assessment for providing assistance." This sentence has been removed from the article in the Acknowledgments Section.

The authors added a full ethical statement and informed consent in the "Methods and methods" section of the article.

Abstract:

- The methods should describe the study sample selection and intervention and define primary outcomes.

1. Study sample selection:

The study sample included 3192 hypertensive patients who were not lost to follow-up from 2011 to 2015.

2. Intervention:

The policy intervention was the increase of NEPHSP subsidy in 2013, and the years before and after 2013 were respectively considered as pre-intervention (2011) and post-intervention (2015).

3. Define primary outcomes

The primary outcomes variables were the outpatient and inpatient expenditure of patients with hypertension, based on direct spending of outpatients and inpatients separately reported by patients with hypertension.

1. The first sentence in the results repeated the last sentence in the methods.

The author has deleted the repeated sentence from the result section：

“Using propensity score matching (PSM) to match the individual characteristics of hypertension in the NEPHSP-covered group and the NEPHSP-uncovered group, difference-in-differences (DID) were used to analyze the outcomes.”

2. the results should describe the major findings quantitatively. Using "significant decrease" alone is not sufficient.

According to the reviewers, the authors have added a quantitative description of the main findings：

A DID estimate of the increased NEPHSP subsidy was associated with a significant decrease of 1251.35 RMB (t=2.13, P=0.034) in hypertension related inpatient expenditure, no significant change (t=0.61, P=0.544) among outpatient expenditure.

Introduction:

1. What is the background of initiating NEPHSP?

As prevention and control of hypertension should be an effective way to reduce deaths, it has been a high priority in China. To address current health challenges, including obesity, hypertension, and non-communicable diseases, and to reduce the disparities for the Chinese population in accessing essential public health services, the Chinese government issued the National Essential Public Health Services Package (NEPHSP) in 2009

2. What are the barriers to hypertension treatment in China?

I think the main barriers to hypertension treatment in China is low awareness and treatment rates：

Several studies using the criteria of the Chinese guidelines for the management of hypertension (systolic BP (SBP) ≥140 mmHg, diastolic BP (DBP) ≥90 mmHg) indicate that less than 50% of patients were aware of their condition, 30–40% were taking prescribed antihypertensive medications, and 7.2–15.3% had achieved control . Using the same nationwide survey but applying the 2017 American College of Cardiology/American Heart Association Guidelines for high blood pressure, hypertension prevalence in China reached 46.4%, while the blood pressure control rate dropped to 3.0% .

- Line 28: What is the time period of incidence?

We increased the time period for prevalence studies：

Two recent studies of hypertension in China suggest that the prevalence of hypertension in China was 23.2% among people aged ≥18 and over from 2012 to 2015, and 44.7% among people aged 35–75 from 2014 to 2017

- Lines 36-37: How do Chinese clinical guidelines define hypertension and treatment goals?

We have added a description of the Chinese guidelines for the management of hypertension.

“At the same time, several studies using the criteria of the Chinese guidelines for the management of hypertension (systolic BP (SBP) ≥140 mmHg, diastolic BP (DBP) ≥90 mmHg) ”

This is a very good suggestion, and we will revise this section of the introduction based on this suggestion：

Previous studies have indicated that after increasing the subsidy of NEPHSP, patients with hypertension covered by the NEPHSP was associated with an increase of 7.9%, 10.3%, and 10.5% in the rate control, medication, and monitoring . Since increasing the subsidy of NEPHSP, the treatment rate and control rate of hypertensive patient increased, so can the outpatient and hospitalization expense of hypertensive patient rise? So far, no relevant research has been conducted.

- Line 50: Using "positively associated with..." is too general. Please describe the findings quantitatively.

we will revise this section based on reviewer suggestion：

Methods:

The population included in the baseline survey was asked the same core questions every two years.

- What information was collected by the survey?

The survey includes a rich set of questions regarding economic standing, physical and psychological health, demographics, and social networks of aged persons.

- What sampling method was used in this survey? Did this survey use a complex survey design?

The CHARLS used a multistage probability sampling approach to select a nationally representative sample. Specifically, the first stage involved random sampling, using the probability-proportional-to-size method, that included all county-level units of China with the exception of Tibet, with the final sample comprising 150 countries. The sample was stratified by region and within region by urban or rural status. In the second stage, administrative villages in rural areas and neighborhoods in urban areas were randomly selected as primary sampling units (PSUs). Three PSUs were selected from each county. In the third stage, 24 households were randomly selected based on the geographical locations and lists of each PSU. In the fourth and final stage, a resident aged ≥45 years was randomly selected from a household, and an interview was conducted with both the selected resident and their spouse .

- What is the purpose of this survey?

CHARLS aims to measure the health status, economic status, and well-being of Chinese residents aged ≥45 years.

- What are the inclusion and exclusion criteria of the study sample?

Patients with hypertension were included in the study.

Patients with hypertension were defined according to "A doctor has told you that you have hypertension," and hypertension was defined according to 2010 Chinese guidelines for the management of hypertension: systolic BP (SBP) ≥140 mmHg, diastolic BP (DBP) ≥90 mm Hg

- Was self-reported hypertension from the diagnosis of hypertension told by doctors or participants' perceptions?

Patients with hypertension were defined according to "A doctor has told you that you have hypertension."

- Line 71: what do you mean all included participants were not covered by NEPHSP?

The treatment group was covered by NEPHSP in 2011 and 2015, and the comparison group were not covered by NEPHSP in 2011 and 2015.

- Line 77: How to define outpatient and inpatient spending?

All outpatient expenditure in the past month was recorded, including both treatment and medication costs.

Inpatient expenditure was defined as all the inpatient costs during the past year, including fees paid to the hospital, ward fees but excluding wages paid to a hired nurse, transportation costs, and accommodation costs for yourself or family members.

To identify total hospitalization expenditure in the past year, total medical cost of doctor visits, and amount paid by their insurance company, we used the following items from the CHARLS baseline questionnaire: “How many times have you received inpatient care?” “How many times did you visit a medical facility?” “What is your total hospitalization cost?” and “What is your total outpatient cost?” If the respondent had two or more inpatient or outpatient treatments in the past year or month, then the respondent was asked to list the total medical costs for all visits.

As the reviewer said, this sentence is easily ambiguous, so the authors remove it and corrected the definitions of inpatient expenses：

As the reviewer stated, the fee is based on the respondent's recollection, and we add limitations as stated below：

Third, outpatient and inpatient costs were based on respondents' recollections and may have been missed and overreported, adversely affecting our estimates. In the future, we will use the medical bills to increase the accuracy of the cost information.

The authors gave a good suggestion.

we did exclude 246 hypertensive patients who were only covered by NEPHSP in 2011 and 8 patients who were only covered by NEPHSP in 2013, based on which we challenged Figure 1.

Additionally, lines 70-71 mentioned all participants were not covered by NEPHSP. How to explain the inconsistency?

Thank the reviewer for pointing out the slip of the pen in the article.

We initially included people with hypertension, if they were covered by NEPHSP in 2011-2015, which enrolled into the treatment group, otherwise enrolled into the comparison group.

The indirect measurement of intervention to define treatment and control groups is a big concern.

That's a problem, but there's plenty of reason to believe this measurement:

1. As mentioned in the S1 table, the NEPHSP policy includes physical examinations paid for by the government.

2. "Who Paid for the physical examination?" in the questionnaire. The answer was 1 self, 2 relatives, 3 units, 4 government, 5 insurances, 6 etc., which distinguishes government payments from other payments.

3. A previous study by Zhang et al . published in the American Journal of Hypertension used the same method.

4. In the fourth limitation, Indirect measurement of intervention in this study would affect the results of the article, and we need to find cleaner interference indicators in the future.

- Line 102: The survey population represents people >=45 years. Why did you use >=65 or not to define age? Shouldn't it be 45-64 and 65+?

As suggested by reviewers, we reanalyzed the data：

The author has been revised to: 45-54, 55-64 and ≥ 65, and reanalyzed the data.

- Line 104: The education level is confusing. Do you mean Middle school represents the highest education level?

As suggested by reviewers, we reanalyzed the data：

Education variables use a harmonized scale that is a simplified version of the 1997 International Standard Classification of Education (ISCED) codes (www.uis.unesco.org): 1. Less than lower secondary education, 2. Upper secondary & vocational training, and 3. Tertiary education.

Based on the reviewers' recommendations：

we classified the health status as 0. Fair; 1. Poor; 2. Good; 3. Very good or Excellent, and reanalyzed the data.

- Line 113: What gap? Do you mean the characteristics of the two groups were significantly different?

Yes, the characteristics of the two groups were significantly different.

As suggested by reviewers, we added：

Patients’ age, sex, marital, work, education, house income, insurance, residence, BMI, health state was matching variables. Logistic regression on patients’ demographics information for each eligible subject was matched according to the propensity score, and because the sample sizes were big difference between the treatment and comparison groups (408 VS. 3167), using the nearest-neighbor method at a 1:4 ratio with 0.02 in the caliper . The 50 times bootstrap method was employed to obtain the robust standard error.

- Lines 125-126: How to define pre- and post- NEPHSP within treatment group? All people in the treatment had been covered by NEPHSP since 2011. Where to get the data before 2011?

Based on respondent received subsidies of RMB 15 from the government, which increased to RMB 30 in 2013, and the government increased support for patients with hypertension from 45 million in 2011 to 70 million in 2013. The pre- and post- intervention within treatment group was defined as before 2013 (2011) and after 2013 (2015), respectively.

We focus on whether the outpatient and hospitalization costs of hypertensive patients covered by NEPHSP changed before and after 2013 when the government increased the subsidy of NEPHSP, without considering the use of data before 2011.

- The outcome measures are not clear. The detailed difference-in-difference in costs should be elaborated. In the linear regression model, what is the dependent variable?

As suggested by reviewers, we revised：

1. A difference-in-differences (DID) method was used to analyze the changes in outpatient and inpatient expense for hypertensive patients before and after NEPHSP subsidy increase.

2. This estimate was computed from a regress model, which includes two dichotomous variables: the time (pre- or post- intervention), the group (treatment or comparison group) and an interaction term between the time and the group. The impact was estimated through the coefficient of this interaction term. The model allows adjustment for potential confounders (age, sex, marital, work, education, house income, insurance, residence, BMI, health state) to reduce residual confounders.

3. Given that the skewed distribution of outpatient and inpatient expense data violated the normal distribution assumption of the ordinary linear model, logarithmic transformation was adopted in the analysis.

Results:

1. Before propensity score matching, what is the original sample size for each group? When the propensity score matching was applied, did you include all participants in the treatment for matching or lose some participants?

2. According to Figure 1, you lost 215 participants in the treatment group after matching. The concern is whether the matched sample can still represent the original study sample. Is there a selection bias in the study design?

First, because the sample sizes were big difference between the treatment and comparison groups (584 vs. 2 608), using the nearest-neighbor method at a 1:4 ratio with 0.02 in the caliper.

Second, we tried 1:1,1:2, 1:3,1:4, and 1:5 matching, we determined the optimal matching 1:4.

Third, we compared the unmatched and matched groups, and no significant differences were found between the two groups, as shown in S2 Table of the Supplementary materials.

It has been modified according to the comments of reviewers. The 2015 variable is deleted, the percentage and 95%CI of each variable in 2011 was displayed, and the chi-square value and P-value were added.

- The results should describe the major findings in Tables and figures quantitatively. Using "significant difference" is too general.

We've stated the chi-square or t values and p-values in all the "significant difference".

- Did you compare the results in Table 3 before and after PSM?

Table 3 presents the results from the DID analyses of outpatient and inpatient expenditure changes between treatment and comparison groups after PSM. Compared to before 2013, the treatment group was associated with a decrease in outpatient expenditure after 2013, but there was no statistically significant (t=0.61, P=0.544). In terms of inpatient expenditure, after 2013, the treatment group was associated with a significant decrease in inpatient expenditure compared with before 2013 (t=2.13, P=0.034).

Conclusion:

- The conclusion should be based on the results, and the conclusion paragraph seems to be about policy implications.

First, the manuscript needs a careful copyediting with occasional misuses of English. I will list some specific examples later.

1. First, because the sample sizes were big difference between the treatment and comparison groups (584 vs. 2 608), using the nearest-neighbor method at a 1:4 ratio with 0.02 in the caliper ,. Second, we tried 1:1,1:2, 1:3,1:4, and 1:5 matching, we determined the optimal matching 1:4.

2. Given that the skewed distribution of outpatient and inpatient expense violated the normal distribution assumption of the ordinary linear model, logarithmic transformation was adopted in the analysis.

Specific Comments

Line 140: Table 1: A t-test comparing the “treatment” and the “control” group would be useful.

Thanks for the reviewer's suggestion. We have revised Table 1 and made chi-square test for treatment group and comparison group.

Line 149: Table 2, “Not poor self-reported health” should be rephrased, maybe “self-reported good or better health”

Thanks for the reviewer's suggestion. We have revised Table 1 and Table 2, divided the self-reported health status into 0. Fair; 1. Poor; 2. Good; 3. Very good or Excellent, and reanalyzed the data

Line 22-23: it should be “in hypertension related inpatient expenditure”

Thanks for the reviewer's suggestion, we have made the revision as required here.

Line 169-173: need a concise title for Figure 4 – could put some of the information in a footnote.

Thanks for the reviewer's suggestion. Here we have changed it to: Fig 4. A. Trends in outpatient expenditure after PSM (RMB). B. Trends in inpatient expenditure after PSM (RMB), and the deleted content was condensed and placed in the main text.

Line 178: The authors occasionally used “compared to” – note that “compare to” highlights similarities while “compare with” stresses on the differences.

Thanks to the reviewer's suggestion, we have changed "compare to" to "compare with" in the full text.

Thanks for the reviewer's suggestion. It has been revised as requested by the reviewer：

1.Sex (Men or Women);

2.Marital status (Cohabitant or Single);

3. Education is a simplified version of the 1997 International Standard Classification of Education (ISCED) codes. (www.uis.unesco.org): 1 = Less than lower secondary education, 2 = Upper secondary & vocational training, and 3 = Tertiary education;

4. Household income was classified by four groups according to the 2011 household income quartile: poor (< RMB 2,320), low income (RMB 2,320–11,200), middle income (RMB 11,200–32,400), and high income (≥ RMB 32,400);

5. We have changed "insurance" to "health insurance".

Participants who had any of the following insurance were defined as having health insurance: 1. Urban employee medical insurance; 2. Urban and rural resident medical insurance; 3. Urban resident medical insurance; 4. New rural cooperative medical insurance; 5. Government medical insurance; 6. Medical aid; 7. Urban non-employed persons's health insurance

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PLoS One. doi: 10.1371/journal.pone.0278026.r003

Decision Letter 1

Jalandhar Pradhan

9 Nov 2022

The impact of increasing expenditure on National Essential Public Health Services on the medical costs of hypertension in China: A difference-in-difference analysis

PONE-D-22-22961R1

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PLoS One. doi: 10.1371/journal.pone.0278026.r004

Acceptance letter

Jalandhar Pradhan

16 Nov 2022

PONE-D-22-22961R1

The impact of increasing expenditure on National Essential Public Health Services on the medical costs of hypertension in China: A difference-in-difference analysis

Dear Dr. Xue:

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

S1 Fig. Test of standardized percentage bias across covariates before and after propensity score matching.

Illustrates the standardized bias across the covariates in the -10% to 10% range across the two groups’ propensity scores before and after matching, and the matching effect was satisfactory.

(TIF)

Click here for additional data file.^{(109.3KB, tif)}

S2 Fig. Test of propensity score distribution before and after propensity score matching.

Illustrates the propensity score distribution across the two groups’ propensity scores before and after matching, and the matching effect was satisfactory.

(TIF)

Click here for additional data file.^{(65KB, tif)}

S3 Fig. Test of propensity score kernel density before propensity score matching.

Illustrates the propensity kernel density across the two groups’ propensity scores before matching.

(TIF)

Click here for additional data file.^{(82.9KB, tif)}

S4 Fig. Test of propensity score kernel density after propensity score matching.

Illustrates the propensity kernel density across the two groups’ propensity scores after matching, and the matching effect was satisfactory.

(TIF)

Click here for additional data file.^{(80.1KB, tif)}

S1 Table. 2009 National Essential Public Health Services Package (NEPHSP) content.

(DOCX)

Click here for additional data file.^{(22.3KB, docx)}

S2 Table. Comparison of samples before and after PSM.

Description of data: Comparison of samples data before and after matching, and the groups no significant difference.

(DOCX)

Click here for additional data file.^{(27.4KB, docx)}

Attachment

Submitted filename: Response to Reviewers.docx

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Data Availability Statement

All the data we used have been publicly released on the CHARLS website: https://charls.charlsdata.com/pages/data/111/zh-cn.html.

[pone.0278026.ref001] 1.Wang Z, Chen Z, Zhang L, Wang X, Hao G, Zhang Z, et al. Status of Hypertension in China: Results From the China Hypertension Survey, 2012–2015. Circulation 2018;137:2344–56. doi: 10.1161/CIRCULATIONAHA.117.032380 [DOI] [PubMed] [Google Scholar]

[pone.0278026.ref002] 2.Lu J, Lu Y, Wang X, Li X, Linderman GC, Wu C, et al. Prevalence, awareness, treatment, and control of hypertension in China: data from 1·7 million adults in a population-based screening study (China PEACE Million Persons Project). Lancet (London, England) 2017;390:2549–58. 10.1016/S0140-6736(17)32478-9. [DOI] [PubMed] [Google Scholar]

[pone.0278026.ref003] 3.Wu Y, Huxley R, Li L, Anna V, Xie G, Yao C, et al. Prevalence, awareness, treatment, and control of hypertension in China: data from the China National Nutrition and Health Survey 2002. Circulation 2008;118:2679–86. doi: 10.1161/CIRCULATIONAHA.108.788166 [DOI] [PubMed] [Google Scholar]

[pone.0278026.ref004] 4.Liu LS, Writing Group of 2010 Chinese Guidelines for the Management of Hypertension. [2010 Chinese guidelines for the management of hypertension]. Zhonghua Xin Xue Guan Bing Za Zhi 2011;39:579–615. 10.1038/ajh.2011.248a. [DOI] [PubMed] [Google Scholar]

[pone.0278026.ref005] 5.Li W, Gu H, Teo KK, Bo J, Wang Y, Yang J, et al. Hypertension prevalence, awareness, treatment, and control in 115 rural and urban communities involving 47 000 people from China. J Hypertens 2016;34:39–46. doi: 10.1097/HJH.0000000000000745 [DOI] [PubMed] [Google Scholar]

[pone.0278026.ref006] 6.Kyu HH, Bachman VF, Alexander LT, Mumford JE, Afshin A, Estep K, et al. Mortality, morbidity, and risk factors in China and its provinces, 1990–2017: a systematic analysis for the Global Burden of Disease Study 2017. Lancet 2019;394:1145–58. doi: 10.1016/S0140-6736(19)30427-1 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0278026.ref007] 7.MSc DE, HBSc CAE, PhD AK, PhD SGA, MB TC, PhD JE, et al. Blood pressure lowering for prevention of cardiovascular disease and death: a systematic review and meta-analysis. Lancet 2015:1–11. [DOI] [PubMed] [Google Scholar]

[pone.0278026.ref008] 8.Wang L, Kong L, Wu F, Bai Y, Burton R. Preventing chronic diseases in China. Lancet 2005;366:1821–4. doi: 10.1016/S0140-6736(05)67344-8 [DOI] [PubMed] [Google Scholar]

[pone.0278026.ref009] 9.Zhang D, Pan X, Li S, Liang D, Hou Z, Li Y, et al. Impact of the National Essential Public Health Services Policy on Hypertension Control in China. Am J Hypertens 2018;31:115–23. 10.1093/ajh/hpx139. [DOI] [PubMed] [Google Scholar]

[pone.0278026.ref010] 10.Ministry of Health. PRC (2009) Opinions on promoting the essential public health services gradually equalization. 2009. http://www.nhc.gov.cn/jws/s3581r/200910/fe1cdd87dcfa4622abca696c712d77e8.shtml?from=singlemessage (accessed October 27, 2020).

[pone.0278026.ref011] 11.Ministry of Health. PRC (2015) Notice on inform national essential public health services in 2015. 2015. http://www.nhc.gov.cn/jws/s3577/201506/61340494c00e4ae4bca0ad8411a724a9.shtml (accessed October 27, 2020).

[pone.0278026.ref012] 12.Pan F, Wang H, Wang Z, Su C, Zhang J, Zhang B. Medical cost attributable to hypertension among Chinese middle and old aged residents in 2011. Wei Sheng Yan Jiu 2017;46:246–50. [PubMed] [Google Scholar]

[pone.0278026.ref013] 13.Bao-rong YF. Analysis on annual change of medical expenses for hypertensive inpatients with different types of health insurance. Chinese Heal Resour 2012;15:406–8. [Google Scholar]

[pone.0278026.ref014] 14.XlONG Ju-yang, CHEN Ke-jan, XIONG Xian-jun et al.. Analysis of the Cost and Influence Factors Urban Hypertensive Patients in China. Chinese Heal Econ 2010;29:81–3. [Google Scholar]

[pone.0278026.ref015] 15.Y. Z, Y. H, J.P. S, J. S, G. Y. Cohort profile: The China health and retirement longitudinal study (CHARLS). Int J Epidemiol 2014;43:61–8. 10.1093/ije/dys203 LK - http://resolver.ebscohost.com/openurl?sid=EMBASE&issn=03005771&id=doi:10.1093%2Fije%2Fdys203&atitle=Cohort+profile%3A+The+China+health+and+retirement+longitudinal+study+%28CHARLS%29&stitle=Int.+J.+Epidemiol.&title=International+Journal+of+Epidemiology&volume=43&issue=1&spage=61&epage=68&aulast=Zhao&aufirst=Yaohui&auinit=Y.&aufull=Zhao+Y.&coden=IJEPB&isbn=&pages=61-68&date=2014&auinit1=Y&auinitm=. [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0278026.ref016] 16.Beaumaster S, Chien S, Lau S, Lin A, Phillps D, Wilkens J, et al. Harmonized CHARLS Documentation 2018;Version C:136. [Google Scholar]

[pone.0278026.ref017] 17.Barba C, Cavalli-Sforza T, Cutter J, Darnton-Hill I, Deurenberg P, Gill T, et al. Appropriate body-mass index for Asian populations and its implications for policy and intervention strategies. Lancet 2004;363:157–63. doi: 10.1016/S0140-6736(03)15268-3 [DOI] [PubMed] [Google Scholar]

[pone.0278026.ref018] 18.Caliendo M, Kopeinig S. Some practical guidance for the implementation of propensity score matching. J Econ Surv 2008;22:31–72. 10.1111/j.1467-6419.2007.00527.x. [DOI] [Google Scholar]

[pone.0278026.ref019] 19.Rosenbaum PR, Rubin DB. The Central Role of the Propensity Score in Observational Studies for Causal Effects. Biometrika 1983;70:41. 10.2307/2335942. [DOI] [Google Scholar]

[pone.0278026.ref020] 20.Xian W, Xu X, Li J, Sun J, Fu H, Wu S, et al. Health care inequality under different medical insurance schemes in a socioeconomically underdeveloped region of China: A propensity score matching analysis. BMC Public Health 2019;19. 10.1186/s12889-019-7761-6. [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0278026.ref021] 21.Karan A, Yip W, Mahal A. Extending health insurance to the poor in India: An impact evaluation of Rashtriya Swasthya Bima Yojana on out of pocket spending for healthcare. Soc Sci Med 2017;181:83–92. doi: 10.1016/j.socscimed.2017.03.053 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0278026.ref022] 22.Godard-Sebillotte C, Karunananthan S, Vedel I. Difference-in-differences analysis and the propensity score to estimate the impact of non-randomized primary care interventions. Fam Pract 2018;36:247–51. 10.1093/fampra/cmz003. [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0278026.ref023] 23.Dimick JB, Ryan AM. Methods for evaluating changes in health care policy: The difference-in-differences approach. JAMA—J Am Med Assoc 2014;312:2401–2. doi: 10.1001/jama.2014.16153 [DOI] [PubMed] [Google Scholar]

[pone.0278026.ref024] 24.Axon DR, Chien J. Assessing healthcare expenditures of older United States adults with pain and poor versus good mental health status: A cross-sectional study. BMJ Open 2022;12. doi: 10.1136/bmjopen-2021-049727 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0278026.ref025] 25.Song Z, Hill C, Bennet J, Vavasis A, Oriol NE. Mobile clinic in massachusetts associated with cost savings from lowering blood pressure and emergency department use. Health Aff 2013;32:36–44. doi: 10.1377/hlthaff.2011.1392 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0278026.ref026] 26.Kirkland EB, Heincelman M, Bishu KG, Schumann SO, Schreiner A, Axon RN, et al. Trends in healthcare expenditures among US adults with hypertension: National estimates, 2003–2014. J Am Heart Assoc 2018;7:e008731. doi: 10.1161/JAHA.118.008731 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0278026.ref027] 27.Li X, Jan S, Yan LL, Hayes A, Chu Y, Wang H, et al. Cost and cost-effectiveness of a school-based education program to reduce salt intake in children and their families in China. PLoS One 2017;12. doi: 10.1371/journal.pone.0183033 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0278026.ref028] 28.Chinese Preventive Medicine Association; Chinese Preventive Medical Association Heart Disease Prevention and Control Professional Committee; Chinese Medical Association Diabetes Society; Stroke Prevention and Control Professional Committee of Chinese Prev. Chinese guidelines for healthy lifestyle prevention of cardiovascular and metabolic diseases. Chi J Prev Med 2020;03:256–77. [Google Scholar]

[pone.0278026.ref029] 29.Yan-qing Liu, Ya-na Bai, Hai-yan Li et al. Study on trends of disease burden of hypertension in Jinchang Cohort. J Lanzhou Univ Sci 2015;41:2015. 10.13885/j.issn.1000-2812.2015.06.007. [DOI] [Google Scholar]

[pone.0278026.ref030] 30.Iso H, Shimamoto T, Naito Y, Sato S, Kitamura A, Iida M, et al. Effects of a long-term hypertension control program on stroke incidence and prevalence in a rural community in Northeastern Japan. Stroke 1998;29:1510–8. doi: 10.1161/01.str.29.8.1510 [DOI] [PubMed] [Google Scholar]

[pone.0278026.ref031] 31.Dong X, Rao J, Ye Y, Pang M, Yang J, Wang H, et al. Trends and associated factors of hypertension among residents aged ≥15 years in Guangzhou, China, 2004–2013. Iran J Public Health 2018;47:198–208. [PMC free article] [PubMed] [Google Scholar]

[pone.0278026.ref032] 32.Center for Health Statistics and Information. China Ministry of Health. An Analysis Report of National Health Services Survey in China, 2013 2013. [Google Scholar]

[pone.0278026.ref033] 33.Dong yunqiu Qian Yun, Xuanxuan Wang et al. Economic Burden of Patients with Hypertension or Type 2 Diabetes Mellitus in Wuxi’ Communities. Chinese Heal Serv Manag 2015;32:18–21. [Google Scholar]

[pone.0278026.ref034] 34.Mingbo Liu, Yichong Li, Shiwei Liu et al. Burden on blood-pressure-related diseases among the Chinese population,in 2010. Chinese J Epidemiol 2014;6:680–3. 10.3760/cma.j.issn.0254-6450.2014.06.014. [DOI] [PubMed] [Google Scholar]

[pone.0278026.ref035] 35.Fang W, Yong-Bin L, Xue D TD. the National Essential Public Health Services project in China: Progress and equity. Chin J Heal Pol 2013;5:9–14. [Google Scholar]

[pone.0278026.ref036] 36.Shane DM, Ayyagari P. Spillover effects of the affordable care act? Exploring the impact on young adult dental insurance coverage. Health Serv Res 2015;50:1109–24. doi: 10.1111/1475-6773.12266 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0278026.ref037] 37.Einav L, Finkelstein A, Ji Y, Mahoney N. Randomized trial shows healthcare payment reform has equal-sized spillover effects on patients not targeted by reform. Proc Natl Acad Sci U S A 2020;117:18939–47. doi: 10.1073/pnas.2004759117 [DOI] [PMC free article] [PubMed] [Google Scholar]

PERMALINK

The impact of increasing expenditure on National Essential Public Health Services on the medical costs of hypertension in China: A difference-in-difference analysis

Long Xue

Mengyun Sui

YunZhen He

Hongzheng Li

Xiaohua Ying

Roles

Abstract

Background

Methods

Results

Conclusions

Introduction

Materials and methods

Data sources

Study population

Interventions

Outcome measurement

Covariates

Statistical analysis

Ethics approval and informed consent statement

Results

Sample characteristics

Fig 1. Flow chart of the participants.

Table 1. Baseline characteristics of patients with hypertension (% 95%CI).

Baseline characteristics after PSM

Table 2. Comparable characteristics between the two groups after PSM.

Outpatient and inpatient expenditure changes pre- and post-NEPHSP

Fig 2. Comparison of outpatient expenditure of the treatment and comparison groups before and after PSM (RMB).

Fig 3. Comparison of inpatient expenditure of treatment and control comparison before and after PSM (RMB).

Common trend assumption

Fig 4.

Comparing the treatment group and the comparison group

Table 3. Impact of the NEPHSP on the outpatient and inpatient expenditure after PSM.

Discussion

Conclusion

Supporting information

Acknowledgments

Data Availability

Funding Statement

References

Decision Letter 0

Jalandhar Pradhan

Roles

Author response to Decision Letter 0

Decision Letter 1

Jalandhar Pradhan

Roles

Acceptance letter

Jalandhar Pradhan

Roles

Associated Data

Supplementary Materials

Data Availability Statement

ACTIONS

PERMALINK

RESOURCES

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