Patient Assessment of Chronic Illness Care (PACIC) and Its Influence on Patient Compliance With Hypertension: A Cross-Sectional Study

Abstract

The Chronic Care Model (CCM) is a framework that supports the proactive, planned, coordinated and patient-centered care of chronic diseases. The Patient Assessment of Chronic Illness Care (PACIC) scale is a valuable tool for evaluating patients’ perspectives on chronic care delivery based on the CCM. Few studies have examined its application in China. This study assesses hypertension care in Chinese patients and explores how PACIC scores relate to patient compliance. A cross-sectional study was conducted in Hangzhou, China, from June to August 2021, including 253 hypertensive patients from 5 county hospitals and 13 primary healthcare centers. The study used the PACIC scale to assess hypertension care delivery and the Compliance of Hypertensive Patients scale (CHPS) to measure patient compliance. Multiple linear regression analyses were used to explore the relationship between demographic characteristics and the total and domain scores of PACIC, as well as the association between CHPS and the domain scores of PACIC. The mean value of overall the PACIC score was 3.12 (out of 5). Problem solving/contextual domain had the highest average score for each item, while follow up/coordination domain had the lowest. Patient activation had negative effects on intention (β = −.18, P < .05), attitude (β = −.21, P < .05), responsibility (β = −.17, P < .05), and the total score of CHPS (β = −.24, P < .01). Delivery system design/decision support was negatively associated with lifestyle (β = −.21, P < .05) and the total score of CHPS (β = −.26, P < .01). Hypertensive patients perceived that they sometimes received hypertension care consistent with the CCM in Chinese primary healthcare settings. A higher level of PACIC score was beneficial for improving hypertensive patient compliance.

Keywords

chronic disease primary healthcare patient compliance hypertension cross-sectional study

What do we already know about this topic?

The Patient Assessment of Chronic Illness Care (PACIC)scale assesses patients’ views on chronic care delivery based on the Chronic Care Model(CCM).

How does your research contribute to the field?

This study evaluates the current situation of hypertension care delivery based on the CCM and explores the association between PACIC scores and patients’ compliance with hypertension.

What are your research’s implications toward theory, practice, or policy?

Hypertensive patients in Chinese primary healthcare settings believed they received some care consistent with the CCM. Higher PACIC scores helped improve patient compliance.

Introduction

Chronic disease is a crucial global public health problem, and hypertension is a highly prevalent chronic condition.^1,2 China faces the challenge of hypertension care. According to Chinese Residents’ Nutrition and Chronic Disease Status Report (2020), approximately 27.5% of residents had hypertension.³ It is concerning that hypertensive patients do not always receive optimal care. The rate of taking prescribed antihypertensive medications among hypertensive adults was only 34.6%, and 12.0% had achieved control in China,⁴ which was lower than rates in Western countries like Canada (72.0% taking medication, 58.3% control) and the USA (88.2% taking medication, 43.7% control).^5
-9 Current hypertension care is often fragmented and event-driven. For instance, there is suboptimal communication and coordination among the multiple healthcare professionals usually involved in a primary care setting.¹⁰ The historical focus on acute care has meant that healthcare delivery did not prioritize improving patients’ self-management skills.¹¹ Therefore, the continuity and coordination of hypertension care should be improved.^10,12

Delivering high-quality chronic care requires significant system changes beyond just adding new features to the existing acute-focused system.¹¹ The Chronic Care Model(CCM) aims to improve care delivery by shifting practices from reactive to proactive, patient-centered care through multidisciplinary team collaboration and planned interactions.^13,14 Hypertension care is a key part of China’s National Basic Public Health Service Programs, combining clinical and public health services in primary healthcare settings.¹⁰ It follows Chinese guidelines for monitoring blood pressure, managing medication, promoting healthy lifestyles, and providing follow-up care through multidisciplinary teams. Furthermore, the Chinese government has implemented a national policy, called Long Term Planning for Chronic Disease Prevention in China (2017-2025), to improve chronic care by integrating patient-related, professionally-directed, and organizational interventions in the primary healthcare system. Hypertension care in China has evolved with links to the CCM.¹⁵ The Patient Assessment of Chronic Illness Care (PACIC) is a widely used tool for evaluating the alignment of chronic care with the CCM from the patient’s perspective, with good reliability and validity across different cultures.^16
-22 The PACIC score can help evaluate patients’ experiences of chronic care delivery, supporting quality improvement efforts in healthcare organizations.²³ However, few studies evaluate the delivery of hypertension care on this scale in primary care settings in China and make cross-culture comparisons.²⁴ Hence, it is meaningful to evaluate the current status of PACIC in China and to provide strategies to improve the quality of chronic care.

PACIC level might be affected by the gender, age, education, monthly income, years of chronic diseases, and the number of disease conditions.^25
-32 However, these factors might vary due to cultural differences. For example, in Singapore, a study among diabetes patients found that having a younger age was positively associated with PACIC scores, while there was no significant difference in education level, gender, and number of chronic diseases.³² In contrast, another study among Turkish patients with chronic diseases found that having a younger age and a higher education level was negatively associated with PACIC scores.²⁹

The PACIC has the potential as a quality improvement tool and patient-centered metric. Nonetheless, its effectiveness would be improved by showing its correlation with other quality measures such as patient compliance and quality of life.³³ Evidence suggests that PACIC scores were related to quality of life.^34,35 Patient compliance is a broad concept. Patient compliance involves patients wanting to change their lifestyle, working closely with healthcare professionals, making treatment decisions together, and adhering to medication.³⁶ Non-compliance can lead to inadequate blood pressure control and is a notable issue in chronic care.^37,38 Previous research has linked the total or domain scores of PACIC with improved medication adherence and compliance with self-management behaviors among diabetes patients.^39
-42 However, more research is needed for other chronic conditions.³¹ In addition, limited evidence has examined the relationship between the domain scores of PACIC and a broad array of compliance, such as intention of care compliance and responsibility compliance. Therefore, it is essential to explore the relationship between the domain scores of PACIC and the above aspects of compliance for patients with hypertension.

The current study: (1) evaluated the current PACIC scores of Chinese patients with hypertension, (2) explored the factors that influence PACIC scores, and (3) investigated the relationships between PACIC and patients’ compliance with hypertension. We hypothesized that the PACIC scores were positively related to almost all the hypertensive patients’ compliance.

Methods

Study Design and Settings

A cross-sectional study was conducted from July to August 2021 in Hangzhou City, Zhejiang Province. We selected 3 districts representing the medical group’s poor, fair, and good construction levels. Each district had 2 or 3 medical groups, and each medical group had one county hospital and several community health service centers. One or 2 medical groups were selected from each district, and 2 or 3 community health centers from central and non-central areas were chosen from each medical group. A total of thirteen community health service centers were chosen as investigation sites. Potential participants were approached while waiting for their appointments in the outpatient clinic. The trained interviewers administered self-designed questionnaires after obtaining oral consent from the participants. Criteria for selecting the participants were as follows: (1) over the age of 18, (2) a diagnosis of hypertension or taking an anti-hypertensive drug, (3) using primary health service over the past 6 months, (4) willing to participate in the study, (5) without serious complications. The exclusion criteria were: (1) not being a local resident, (2) not having lived in the area for more than half a year, and (3) inability to communicate in Chinese. To confirm associations, it is recommended to have at least 10 events per analyzed variable.^43,44 In our linear regression model, there were 15 predictor variables, including dummy variables, necessitating a sample size of 167 after accounting for a 10% estimation of invalid questionnaires. A total of 265 questionnaires were distributed, and 253 valid questionnaires were returned, resulting in a 95.47% response rate.

Measurements

The PACIC scale, developed by Glasgow et al. was designed to measure the patient assessment of care for chronic conditions according to the CCM. It assesses the behavior of healthcare professionals and practice teams from the perspective of patients.¹⁶ This scale is comprised of 20 items, covering 5 domains: (1) patient activation (3 items, eg., “asked to talk about any problems with my medication history or their effects”), (2) delivery system design/decision support (3 items, eg., “shown the way I took care of my illness influenced my condition”), (3) goal setting/tailoring (5 items, eg., “helped to set specific goals to improve my eating or exercise”), (4) problem solving/contextual (4 items, eg., “asked to how my chronic illness affects my life”), (5) follow up/coordination (5 items, eg., “encouraged to take part in programs in the community that could help me recover from the disease”). Participants were required to rate on a 5-point Likert scale, ranging from 1 (almost never) to 5 (almost always). Total scores range from 20 to 100, with higher scores indicating a more frequent occurrence of the pertinent aspect of chronic care and a better perception of chronic care delivery.²⁰ The Chinese version of the PACIC scale has been validated in multiple places within several provinces of China. For example, a study in Anhui province among 3434 hypertensive patients suggested good internal reliability (Cronbach’s alpha = .88).²⁴ More information on PACIC is shown in the Supplemental File.

The Compliance of Hypertensive Patients scale (CHPS) developed by Lahdenperä et al. is one of the most commonly used instruments to evaluate hypertension compliance.⁴⁵ In our study, the compliance of patients with hypertension was measured by the Chinese version of the CHPS.⁴⁶ To be specific, this scale comprises 13 items across 5 domains: Intention (4 items), Lifestyle (3 items), Attitude (3 items), Responsibility (2 items), and Smoking (1 item). Responses are rated on a 4-point Likert-type scale from 1 (good compliance) to 4 (poor compliance). Total scores range from 13 to 52, where a lower score indicates a higher-level compliance. This version of the CHPS has been used in other Chinese research and found to be reliable in measuring hypertension compliance.^46,47 The Cronbach’s alpha was .80 in our study. More information on the CHPS is shown in the Supplemental File.

A self-designed questionnaire was produced to collect demographic characteristics, which include the gender, age, education level, monthly income, years of hypertension, and number of chronic diseases (see Supplemental File for more details).

Statistical Analysis

Data were entered through EpiData 3.1 (The Epidata Association, Odense, Denmark) and analyzed using SPSS 26.0 (SPSS Inc., Chicago, IL, USA). Firstly, descriptive analyses were performed for socio-demographics. Categorical data were listed as frequency and percentage, while continuous data were shown as mean and standard deviation (SD). Secondly, a multiple linear regression was used to analyze the association between demographic characteristics, domain and the total scores of PACIC. Another multiple linear regression was conducted to explore the association between CHPS and the domain scores of PACIC after controlling socio-demographics (the Enter method was used in the multiple linear regression model). As the total and domain scores of PACIC and CHPS were not normally distributed, the data were submitted to the Z-score transformation before entering into the analysis. Multicollinearity was assessed by checking the variance inflation factor (VIF) in a multiple regression model. Variance inflation factors below 5 in our model indicated a low risk of multicollinearity.⁴⁸ Model explanatory power was evaluated with R². The significant level of all the regressions was set at P < .05.

Results

Sample Characteristics

A total of 253 patients with hypertension were eligible. The social-demographic characteristics are shown in Table 1. The mean age of the participants was 65.28 ± 11.80 years, 56.13% were male, 62.10% had a primary education or below, and 63.05% of the participants’ monthly income was less than 3000 RMB. Among the participants, 47.04% had been diagnosed with hypertension for more than 7 years, and 49.80% had co-morbidity diseases.

Table 1.

Demographic Characteristics of the Participants (n, %).

Variable	Values
Gender
Male	142 (56.13)
Female	111 (43.87)
Age group
Less than 59 years	70 (27.67)
60-74 years	133 (52.60)
75-89 years	50 (19.76)
Education level*
Primary school and below	154 (62.10)
Middle school	58 (23.39)
High school above	36 (14.52)
Monthly income*
Less than 3000 RMB	157 (63.05)
3001-5000 RMB	51 (20.48)
More than 5001 RMB	41 (16.47)
Years of hypertension
Less than 3 years	80 (31.62)
4-6 years	54 (21.34)
More than 7 years	119 (47.04)
Number of chronic conditions
1 condition	127 (50.20)
2 conditions	87 (34.39)
More than 3 conditions	39 (15.42)

Missing data < 3%.

Scores on the PACIC Scale

In our study, Cronbach’s alpha for the PACIC scale was .89. Scores on the PACIC scale are shown in Table 2. The total score for PACIC was 62.49 (13.25), and the mean value of the overall PACIC score was 3.12 (0.66). The domain scores on the PACIC scale were as follows: patient activation was 10.43 (2.68), delivery system design/decision support was 9.78 (2.30), goal setting/tailoring was 15.00 (3.79), problem solving/contextual was 14.03 (3.45), and follow up/coordination was 13.12 (4.55). The problem solving/contextual domain had the highest average score for each item (3.51 (0.86)), while the follow up/coordination domain had the lowest average score for each item (2.62 (0.91)).

Table 2.

The Total and Domain Scores of the PACIC Scale.

Dimension	Item	Range	Average score of each domain $\bar{x} - S D$	The average score of each item $\bar{x} - S D$
Patient activation	3	3-15	10.43 (2.68)	3.48 (0.89)
Delivery system design/decision support	3	3-15	9.78 (2.30)	3.26 (0.77)
Goal setting/tailoring	5	5-25	15.00 (3.79)	3.00 (0.76)
Problem solving/contextual	4	4-20	14.03 (3.45)	3.51 (0.86)
Follow up/coordination	5	5-25	13.12 (4.55)	2.62 (0.91)
PACIC scale	20	20-100	62.49 (13.25)	3.12 (0.66)

The Influence of Socio-Demographic Characteristics on PACIC Scores

The association between socio-demographic characteristics and PACIC scores was analyzed by multiple linear regression and shown in Table 3. The PACIC scores were positively associated with age (β = .34, P < .001), having a middle school education level (β = .16, P < .05, Ref: primary and below), and having a high school and above education level (β = .28, P < .01, Ref: primary and below), and were negatively associated with more than 3 chronic conditions (β = −.20, P < .01, Ref: one chronic condition). For domain scores, delivery system design/decision support was positively associated with age (β = .33, P < .001), having a middle school education level (β = .21, P < .01, Ref: primary and below) and having a high school and above education level (β = .37, P < .001, Ref: primary and below), and was negatively associated with more than 3 chronic conditions (β = −.24, P < .01, Ref: one chronic condition). Goal setting/tailoring was positively associated with age (β = .34, P < .001), having a middle school education level (β = .15, P < .05, Ref: primary and below) and having a high school and above education level (β = .28, P < .01, Ref: primary and below), and was negatively associated with a monthly income of 3001 to 5000 RMB (β = −.15, P < .05; Ref: less than 3000 RMB), and more than 3 chronic conditions (β = −.19, P < .01, Ref: one chronic condition). Problem solving/contextual was positively associated with age (β = .30, P < .01), having a middle school education level (β = .16, P < .05, Ref: primary and below) and having a high school and above education level (β = .26, P < .01, Ref: primary and below), and was negatively associated with more than 3 chronic conditions (β = −.15, P < .05, Ref: one chronic condition). Follow up/coordination was positively associated with age (β = .19, P < .05).

Table 3.

Liner Regression of Patient Characteristics on the Total and Domain Scores of PACIC Scale (Standardized β Value).

Variable	Patient activations	Delivery system design/Decision support	Goal setting/Tailoring	Problem solving/Contextual	Follow up/Coordination	PACIC scale
Gender
Male	1	1	1	1	1	1
Female	−0.001	0.11	0.04	0.04	0.04	0.05
Age	0.14	0.33***	0.34***	0.30**	0.19*	0.34***
Education level
Primary school and below	1	1	1	1	1	1
Middle school	0.08	0.21**	0.15*	0.16*	0.07	0.16*
High school above	0.15	0.37***	0.28**	0.26**	0.13	0.28**
Monthly income
Less than 3000 RMB	1	1	1	1	1	1
3001-5000 RMB	−0.03	−0.01	−0.15*	−0.02	−0.06	−0.08
More than 5001 RMB	0.01	−0.01	−0.01	−0.05	−0.01	−0.01
Years of hypertension
Less than 3 years	1	1	1	1	1	1
4-6 years	0.05	0.01	0.07	−0.03	0.09	0.05
More than 7 years	0.001	−0.09	−0.10	−0.08	−0.01	−0.08
Number of chronic conditions
1 condition	1	1	1	1	1	1
2 conditions	−0.07	−0.03	−0.13	−0.02	−0.12	−0.11
More than 3 conditions	−0.11	−0.24**	−0.19**	−0.15*	−0.10	−0.20**
R ²	.03	.15	.10	.08	.04	.10

P < .05. **P < .01. ***P < .001.

The Influence of PACIC Scores on CHPS Scores

The associations between the PACIC scores and CHPS scores are presented in Table 4. After adjusting for gender, age, education level, monthly income, years of hypertension, and number of chronic conditions, patient activation had negative effects on intention (β = −.18, P < .05), attitude (β = −.21, P < .05), responsibility (β = −.17, P < .05) and the total score of CHPS (β = −.24, P < .01). Delivery system design/decision support was negatively associated with lifestyle (β = −.21, P < .05) and the total score of CHPS (β = −.26, P < .01). Goal setting/tailoring was positively associated with smoking (β = .29, P < .01).

Table 4.

Liner Regression of Domain Scores of PACIC on Total and Domain Scores of CHPS (Standardized β Value).

Variables	Intention	Lifestyle	Attitude	Responsibility	Smoking	Total scale
Patient activations	−0.18*	−0.13	−0.21*	−0.17*	−0.02	−0.24**
Delivery system design/Decision support	−0.09	−0.21*	−0.14	−0.18	−0.09	−0.26**
Goal setting/Tailoring	−0.06	−0.01	0.18	0.15	0.29**	0.15
Problem solving/Contextual	−0.11	−0.06	−0.01	0.04	−0.04	−0.04
Follow up/Coordination	0.10	0.06	0.04	−0.004	−0.14	0.05
R ²	0.11	0.17	0.10	0.07	0.32	0.18

Adjusted for gender, age, education level, monthly income, years of hypertension, and number of chronic conditions.

P < .05. **P < .01.

Discussion

This study assessed the current situation of hypertension care based on the PACIC scale in China and first explored the relationship between PACIC scores and broader hypertension compliance. The CCM is promoted as a care template for the chronically ill to improve care.¹³ The total score on PACIC among hypertensive patients in our study was 62.49 (13.25), and the mean value of the overall PACIC score was 3.12 (0.66), indicating that hypertensive patients perceived that they sometimes received hypertension care consistent with the CCM in primary healthcare settings.³² A PACIC survey score of 3.5 or higher was indicative of high concordance with the CCM, as demonstrated in previous research.⁴⁹ However, our findings showed that the elements of the CCM have not yet been fully implemented in primary healthcare settings. Compared with previous literature, the mean value of the overall PACIC score among patients with common chronic diseases was slightly lower than in some high-income countries.^32,50 That might be because Chinese primary healthcare providers and patients had sufficient awareness of hypertension prevention and control due to the late launch of the hypertension prevention and control system in China.⁵¹

Among the 5 subscales of PACIC, problem solving/contextual and patient activation elements of the CCM had higher scores, indicating a strong relationship between hypertensive patients and their healthcare professionals in sharing treatment decisions in hypertension care. This finding was not consistent with other literature, which suggested that the delivery system design/support decision element of CCM, related to health promotion and self-management, was their highest subscale.^19,23,50,52 One potential explanation was that Chinese primary healthcare providers might prioritize providing treatment services for hypertensive patients over conducting health education, possibly due to the medical-oriented nature of their performance evaluations.¹² Another explanation was that Chinese primary healthcare providers had lower health education capacity than those in Western countries.^53,54,55 However, other elements, such as goal setting and follow up, had lower scores, similar to the results from other countries.^19,29,32,55

Findings from the current study suggested that factors influencing the PACIC scores included age, education level, and number of chronic conditions. Patients in older age groups demonstrated significantly higher scores in nearly all domains and total PACIC scores compared to their younger age groups, consistent with some studies,^26,29,56 but not with other studies.^30
-32 Patients with higher educational levels were positively associated with nearly all domains and total PACIC scores, similar to some other studies.^28,57 This might be attributed to the likelihood that individuals with higher levels of education had greater health literacy and comprehension abilities during medical consultations.⁵⁷ The number of chronic conditions in our sample was negatively associated with nearly all domains and total PACIC scores, consistent with some studies,^28,58 but contrary to Western studies.^59,60 This might be because hypertensive patients with other chronic diseases might have higher expectations for more comprehensive care services from primary healthcare providers than those with only hypertension. However, primary healthcare providers struggled to cope with patients with multiple chronic conditions in China.^11,61 Our finding of no significant association between the domain and total PACIC scores and gender is consistent with the findings of some studies,^23,29,32,62 but not in a Danish study where the total PACIC score was higher in males than females.³⁰ Various socioeconomic factors and differences in healthcare systems could influence these differences. Future research on healthcare quality should consider patient ratings of care received alongside data on care provided, as this could provide a more comprehensive understanding of healthcare delivery.

Previous studies have shown that the PACIC scores were associated with some aspects of compliance.^41,42,63 We also found similar results in our research. The patient activation element of the CCM was negatively associated with almost all compliance domains, indicating that a higher patient activation score was beneficial for promoting compliant behavior in hypertension.^56,64 Effective communications between hypertensive patients and their primary healthcare providers about treatment plans positively influenced patients’ attitudes and self-efficacy, ultimately leading to improved compliance.^65,66 Furthermore, the delivery system/support decision element of the CCM was an important influencing factor in lifestyle and total hypertension compliance, consistent with studies.^40,67 That could be because primary healthcare providers inform hypertensive patients about effective prevention strategies for chronic lifestyle-related diseases and assist them in understanding how health-related behaviors impact chronic disease risk factors. This could enhance patients’ appreciation of the importance of lifestyle changes and potentially improve their overall compliance.⁶⁸

There were some limitations in our study. Firstly, considering our study population consisted predominantly of older patients with hypertension and lower educational status, we had to consider the possibility that the questionnaire might not have been clear enough for some participants to understand. Secondly, our study was conducted among Chinese patients with hypertension from only one city in Zhejiang province. This geographical limitation could affect the generalizability of the findings. Thirdly, our study did not assess inter-rater reliability among the interviewers, which might impact the strength of our results. Future research should use statistical measures like Cohen’s Kappa or intraclass correlation coefficient (ICC) to confirm data consistency.

Conclusions

The Chinese government has been developing strategies based on the CCM to improve chronic care, but the elements of the CCM have not yet been fully implemented in primary healthcare settings. Our findings showed that there was a lack of setting specific goals and arranging follow-ups for hypertensive patients. This required a well-collaborative multidisciplinary team with the general practitioner as the core and other healthcare professionals such as nurses, nutritionists, and pharmacists as members to provide integrated services for hypertensive patients. We demonstrated that the care for patients with multiple chronic conditions was insufficient in primary healthcare settings, so healthcare managers and policymakers should focus on capacity of the primary healthcare settings. We provided evidence to the primary healthcare professionals that their efforts in building good communication with patients to share treatment decisions could improve compliance with hypertension. It is essential for primary healthcare professionals to train their communication skills. Furthermore, to enhance patient compliance, it is recommended that primary healthcare professionals increase the frequency of health education and explain the link between health behaviors and disease outcomes.

Supplemental Material

sj-docx-1-inq-10.1177_00469580241277445 – Supplemental material for Patient Assessment of Chronic Illness Care (PACIC) and Its Influence on Patient Compliance With Hypertension: A Cross-Sectional Study

Supplemental material, sj-docx-1-inq-10.1177_00469580241277445 for Patient Assessment of Chronic Illness Care (PACIC) and Its Influence on Patient Compliance With Hypertension: A Cross-Sectional Study by Chi Zhou, Fang Tan, Si Hong Lai, Jing Chun Chen, Chao Yi Chen, Gao Feng Zhang and Yin Dong in INQUIRY: The Journal of Health Care Organization, Provision, and Financing

Footnotes

Acknowledgements

We thank all the patients who participated in this study.

Author’s Note

Si Hong Lai is also affiliated to School of Medical Imaging, Hangzhou Medical College, Hangzhou, Zhejiang, China.

Jing Chun Chen is also affiliated to School of Health Medicine, Guangzhou Huashang College, Guangzhou, Guangdong, China.

Abbreviations

PACIC Patient Assessment of Chronic Illness Care

CHPS Compliance of Hypertensive Patients scale

SD Standard Deviation

CCM Chronic Care Model

VIF Variance Inflation Factor

ICC Intraclass Correlation Coefficient

Data Availability Statement

The data can be requested from the corresponding author. All materials used for the purpose of this study are available at the request of the corresponding author.

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This study was supported by the MOE (Ministry of Education in China) Project of Humanities and Social Sciences (No: 22YJCZH262), the National Natural Science Foundation of China (No: 72274050), and the 2023 Social Development Science and Technology Program Project of Yuhuan City (No: 202307).

Ethical Considerations

This study was approved by the Ethics Committee of Hangzhou Normal University (No. 2019065, 2022-1118).

Consent to Participate

All study methods were performed in accordance with relevant guidelines and regulations. Before enrollment, each participant was required to provide verbal informed consent.

ORCID iD

Yin Dong

Supplemental Material

Supplemental material for this article is available online.

References

Kario

Okura

Hoshide

Mogi

The WHO Global report 2023 on hypertension warning the emerging hypertension burden in globe and its treatment strategy. Hypertens Res. 2024;47(5):1099-1102. doi:10.1038/s41440-024-01622-w

Mancia

Kreutz

Brunström

2023 ESH Guidelines for the management of arterial hypertension The Task Force for the management of arterial hypertension of the European Society of Hypertension: endorsed by the International Society of Hypertension (ISH) and the European Renal Association (ERA). J Hypertens. 2023;41(12):1874-2071. doi:10.1097/HJH.0000000000003480

Chinese National Health and Family Planning Commission. Chinese Residents’ Nutrition and Chronic Disease Status Report. People’s Medical Publishing House; 2020:2020.

Zhang

Shi

Zhou

, et al. Prevalence, awareness, treatment, and control of hypertension in China, 2004-18: findings from six rounds of a national survey. BMJ. 2023;380:e071952. doi:10.1136/bmj-2022-071952

Zhou

Carrillo-Larco

Danaei

, et al. Worldwide trends in hypertension prevalence and progress in treatment and control from 1990 to 2019: a pooled analysis of 1201 population-representative studies with 104 million participants. Lancet. 2021;398(10304):957-980. doi:10.1016/S0140-6736(21)01330-1

Zhou

Danaei

Stevens

, et al. Long-term and recent trends in hypertension awareness, treatment, and control in 12 high-income countries: an analysis of 123 nationally representative surveys. Lancet. 2019;394(10199):639-651. doi:10.1016/S0140-6736(19)31145-6

Abdalla

Bolen

Brettler

, et al. Implementation strategies to improve blood pressure control in the United States: a Scientific Statement from the American Heart Association and American Medical Association. Hypertension. 2023;80(10):e143-e157. doi:10.1161/HYP.0000000000000232

Leung

Williams

JVA

McAlister

Campbell

NRC

Padwal

; Hypertension Canada’s Research and Evaluation Committee. Worsening hypertension awareness, treatment, and control rates in Canadian women between 2007 and 2017. Can J Cardiol. 2020;36(5):732-739. doi:10.1016/j.cjca.2020.02.092

Gijón-Conde

Sánchez-Martínez

Graciani

, et al. Impact of the European and American guidelines on hypertension prevalence, treatment, and cardiometabolic goals. J Hypertens. 2019;37(7):1393-1400. doi:10.1097/HJH.0000000000002065

10.

Krumholz

Yip

, et al. Quality of primary health care in China: challenges and recommendations. Lancet. 2020;395(10239):1802-1812. doi:10.1016/S0140-6736(20)30122-7

11.

Cramm

Nieboer

AP.

High-quality chronic care delivery improves experiences of chronically ill patients receiving care. Int J Qual Health Care. 2013;25(6):689-695. doi:10.1093/intqhc/mzt065

12.

, et al. The primary health-care system in China. Lancet. 2017;390(10112):2584-2594. doi:10.1016/S0140-6736(17)33109-4

13.

Wagner

Austin

Korff

MV.

Organizing care for patients with chronic illness. Milbank Q. 1996;74(4):511. doi:10.2307/3350391

14.

Grover

Joshi

An overview of chronic disease models: a systematic literature review. Glob J Health Sci. 2014;7(2):210-227. doi:10.5539/gjhs.v7n2p210

15.

Zhang

Yang

Implications of domestic and foreign typical chronic disease management models for rural chronic disease management in China. Chin Gen Pr. 2022;25(16):1935-1941. doi:10.12114/j.issn.1007-9572.2021.00.327

16.

Glasgow

Wagner

Schaefer

, et al. Development and validation of the Patient Assessment of Chronic Illness Care (PACIC). Med Care. 2005;43(5):436-444. doi:10.1097/01.mlr.0000160375.47920.8c

17.

Seo

Kim

Park

KS.

Psychometric properties and effects on health outcomes of the Patient Assessment of Chronic Illness Care (PACIC) in Korean hemodialysis patients. Healthcare. 2022;10(6):1149. doi:10.3390/healthcare10061149

18.

Boecker

Iglesias

Peytremann-Bridevaux

Psychometric evaluation of the Patient Assessment of Chronic Illness Care instrument using item analysis according to the Rasch model. J Eval Clin Pract. 2022;28(4):670-679. doi:10.1111/jep.13662

19.

Schwenke

Welzel

Luck-Sikorski

, et al. Psychometric properties of the Patient Assessment of Chronic Illness Care measure (PACIC-5A) among patients with obesity. BMC Health Serv Res. 2019;19(1):61. doi:10.1186/s12913-019-3871-1

20.

Pilipovic-Broceta

Vasiljevic

Marinkovic

, et al. Assessment of hypertension chronic care model: pacic application in Bosnia and Herzegovina. PLoS One. 2018;13(8):e0202250. doi:10.1371/journal.pone.0202250

21.

Arditi

Iglesias

Peytremann-Bridevaux

The use of the patient assessment of Chronic Illness Care (PACIC) instrument in diabetes care: a systematic review and meta-analysis. Int J Qual Health Care. 2018;30(10):743-750. doi:10.1093/intqhc/mzy091

22.

Törő

Kósa

Takács

, et al. Patients’ Assessment of Chronic Illness Care (PACIC): validation and evaluation of PACIC Scale among patients with type 2 diabetes in Hungary. Int J Integr Care. 2022;22(3):5. doi:10.5334/ijic.6010

23.

Hazazi

Wilson

Improving management of non-communicable chronic diseases in primary healthcare centres in the Saudi Health Care System. Health Serv Insights. 2022;15:117863292210886. doi:10.1177/11786329221088694

24.

Ying

Tang

Zhang

, et al. The assessment of hypertension management in Yuexi County based on PACIC scale. Chin Rural Heal Serv Adm. 2021;41(8):592-596. doi:10.19955/j.cnki.1005-5916.2021.08.013

25.

Wallace

Perkhounkova

Tseng

Schillinger

Influence of patient characteristics on assessment of diabetes self-management support. Nurs Res. 2013;62(2):106-114. doi:10.1097/NNR.0b013e3182843b77

26.

Balbale

Etingen

Malhiot

Miskevics

LaVela

SL.

Perceptions of chronic illness care among veterans with multiple chronic conditions. Mil Med. 2016;181(5):439-444. doi:10.7205/MILMED-D-15-00207

27.

Koley

Saha

Ghosh

, et al. Patient-Assessed Chronic Illness Care (PACIC) scenario in an Indian homeopathic hospital. J Tradit Complement Med. 2016;6(1):72-77. doi:10.1016/j.jtcme.2014.11.020

28.

Dayapoglu

Assessment of health care quality in Chronic Neurological Diseases: stroke, multiple sclerosis and epilepsy. J Neurol Neurophysiol. 2016;07(6):1000402. doi:10.4172/2155-9562.1000402

29.

Akbaş

Sürücü

ŞG

Akça

Köroğlu

. Evaluation of chronic patient nursing care by the patients: a university hospital case. Life Int J Heal Sci. 2017;3(2):38-50. doi:10.20319/lijhls.2017.32.3850

30.

Jensen

Bro

Nicolaisen

Maindal

HT.

Higher patient assessed quality of chronic care is associated with lower diabetes distress among adults with early-onset type 2 diabetes: cross-sectional survey results from the Danish DD2-study. Prim Care Diabetes. 2020;14(5):522-528. doi:10.1016/j.pcd.2020.02.003

31.

Söylemez

Aşılar

RH.

The relationship between hypertensive patients’ satisfaction with hypertension care and their antihypertensive medication adherence. J Vasc Nurs. 2023;41(3):81-88. doi:10.1016/j.jvn.2023.05.002

32.

Goh

Szücs

Siah

, et al. Patient perspectives of diabetes care in primary care networks in Singapore: a mixed-methods study. BMC Health Serv Res. 2023;23(1):1445. doi:10.1186/s12913-023-10310-3

33.

Schmittdiel

Mosen

Glasgow

, et al. Patient Assessment of Chronic Illness Care (PACIC) and improved patient-centered outcomes for chronic conditions. J Gen Intern Med. 2008;23(1):77-80. doi:10.1007/s11606-007-0452-5

34.

Malliarou

Desikou

Lahana

, et al. Diabetic patient assessment of chronic illness care using PACIC. BMC Health Serv Res. 2020;20(1):543. doi:10.1186/s12913-020-05400-5

35.

Akan

Kartal

The association between Patient Assessment of Chronic Illness Care Scores and quality of life in type 2 diabetes patients. J Res Nurs. 2023;28(3):199-211. doi:10.1177/17449871221144899

36.

Kyngäs

Lahdenperä

Compliance of patients with hypertension and associated factors. J Adv Nurs. 1999;29(4):832-839. doi:10.1046/j.1365-2648.1999.00962.x

37.

Murali

Mullan

Chen

Roodenrys

Lonergan

Medication adherence in randomized controlled trials evaluating cardiovascular or mortality outcomes in dialysis patients: a systematic review. BMC Nephrol. 2017;18(1):42. doi:10.1186/s12882-017-0449-1

38.

Emre

Edirne

Özşahin

Barışkan

Çoban

The relationship between adherence to medication and quality of life and health perception in hypertensive patients. Turk J Family Med Prim Care. 2020;14(3):436-442. doi:10.21763/tjfmpc.725974

39.

Mackey

Parchman

Leykum

, et al. Impact of the chronic care model on medication adherence when patients perceive cost as a barrier. Prim Care Diabetes. 2012;6(2):137-142. doi:10.1016/j.pcd.2011.12.004

40.

Liu

Sha

Wang

Patient assessment of chronic illness care, glycemic control and the utilization of community health care among the patients with type 2 diabetes in Shanghai, China. PLoS One. 2013;8(9):e73010. doi:10.1371/journal.pone.0073010

41.

Kinney

Lemon

Person

Pagoto

Saczynski

JS.

The association between patient activation and medication adherence, hospitalization, and emergency room utilization in patients with chronic illnesses: a systematic review. Patient Educ Couns. 2015;98(5):545-552. doi:10.1016/j.pec.2015.02.005

42.

Aung

Ostini

Dower

, et al. Patient Assessment of Chronic Illness Care (PACIC) in type 2 diabetes: a longitudinal study. Eval Health Prof. 2016;39(2):185-203. doi:10.1177/0163278714556674

43.

Pavlou

Ambler

Seaman

, et al. How to develop a more accurate risk prediction model when there are few events. BMJ. 2015;351:h3868. doi:10.1136/bmj.h3868

44.

Harrell

Lee

Califf

Pryor

Rosati

RA.

Regression modelling strategies for improved prognostic prediction. Stat Med. 1984;3(2):143-152. doi:10.1002/sim.4780030207

45.

Lahdenperä

Wright

Kyngäs

HA.

Development of a scale to assess the compliance of hypertensive patients. Int J Nurs Stud. 2003;40(7):677-684. doi:10.1016/S0020-7489(02)00110-4

46.

Zhang

Fang

, et al. Adherence with blood pressure monitoring wearable device among the elderly with hypertension: the case of rural China. Brain Behav. 2020;10(6):e01599. doi:10.1002/brb3.1599

47.

Zhang

Tan

Compliance with care and poor sleep quality among hypertensive adults in a mountainous area of Hubei Province, People’s Republic of China. Patient Prefer Adherence. 2019;13:1221-1227. doi:10.2147/PPA.S212241

48.

Marill

KA.

Advanced Statistics: linear regression, part I: simple linear regression. Acad Emerg Med. 2004;11(1):87-93. doi:10.1111/j.1553-2712.2004.tb01378.x

49.

Jackson

Weinberger

Hamilton

Edelman

Racial/ethnic and educational-level differences in diabetes care experiences in primary care. Prim Care Diabetes. 2008;2(1):39-44. doi:10.1016/j.pcd.2007.11.002

50.

Jackson

Stechuchak

Weinberger

, et al. How views of the organization of primary care among patients with hypertension vary by race or ethnicity. Mil Med. 2018;183(9-10):e583-e588. doi:10.1093/milmed/usx111

51.

Wang

, 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. 2017;390(10112):2549-2558. doi:10.1016/S0140-6736(17)32478-9

52.

Silva

Soares

Silva

PAB

, et al. Assessment of the quality of primary care for the elderly according to the chronic care model. Rev Latinoam Enfermagem. 2018;26:e2987. doi:10.1590/1518-8345.2331.2987

53.

Bonomi

Wagner

Glasgow

VonKorff

Assessment of Chronic Illness Care (ACIC): a practical tool to measure quality improvement. Health Serv Res. 2002;37(3):791-820. doi:10.1111/1475-6773.00049

54.

Wang

Sun

, et al. Status of capacity for chronic disease management among medical staff in grass-roots heatlth care institutions in Hunan province. Chin J Public Heal. 2016;32(9):1231-1234. doi:10.11847/zgggws2016-32-09-25

55.

Desmedt

Vertriest

Petrovic

, et al. Seen through the patients’ eyes: quality of chronic illness care. Fam Pract. 2018;35(4):446-451. doi:10.1093/fampra/cmx123

56.

Bombard

Baker

Orlando

, et al. Engaging patients to improve quality of care: a systematic review. Implement Sci. 2018;13(1):98. doi:10.1186/s13012-018-0784-z

57.

Lim

YMF

Teh

, et al. Patient experience on self-management support among primary care patients with diabetes and hypertension. Int J Qual Health Care. 2019;31(7):37-43. doi:10.1093/intqhc/mzy252

58.

Drewes

de Jong-van Til

Struijs

, et al. Measuring chronic care management experience of patients with diabetes: PACIC and PACIC+ validation. Int J Integr Care. 2012;12(8):e194. doi:10.5334/ijic.862

59.

Petersen

Paulitsch

Mergenthal

, et al. The MultiCare Study Group. Implementation of chronic illness care in German primary care practices – how do multimorbid older patients view routine care? A cross-sectional study using multilevel hierarchical modeling. BMC Health Serv Res. 2014;14(1):336. doi:10.1186/1472-6963-14-336

60.

Houle

Beaulieu

Lussier

, et al. Patients’ experience of chronic illness care in a network of teaching settings. Can Fam Physician Med Fam Can. 2012;58(12):1366-1373.

61.

Liu

Fang

Study on potential factors of patient satisfaction: based on exploratory factor analysis. Patient Prefer Adherence. 2019;13:1983-1994. doi:10.2147/PPA.S228073

62.

Lukaschek

Beltz

Rospleszcz

, et al. Depressive primary care patients’ assessment of received collaborative care. Sci Rep. 2023;13(1):2329. doi:10.1038/s41598-023-29339-9

63.

Aung

Donald

Williams

Coll

Doi

SAR

. Joint influence of patient-assessed chronic illness care and patient activation on glycaemic control in type 2 diabetes. Int J Qual Health Care. 2015;27(2):117-124. doi:10.1093/intqhc/mzv001

64.

Świątoniowska-Lonc

Polański

Tański

Jankowska-Polańska

Impact of satisfaction with physician-patient communication on self-care and adherence in patients with hypertension: cross-sectional study. BMC Health Serv Res. 2020;20(1):1046. doi:10.1186/s12913-020-05912-0

65.

Tavakoly Sany

Behzhad

Ferns

Peyman

Communication skills training for physicians improves health literacy and medical outcomes among patients with hypertension: a randomized controlled trial. BMC Health Serv Res. 2020;20(1):60. doi:10.1186/s12913-020-4901-8

66.

Shakya

Self-efficacy among patients with chronic diseases and its associated factors. J Kathmandu Med Coll. 2018;7(3):82-88.

67.

Alefan

Huwari

Alshogran

Jarrah

MI.

Factors affecting hypertensive patients’ compliance with healthy lifestyle. Patient Prefer Adherence. 2019;13:577-585. doi:10.2147/PPA.S198446

68.

Hughes

Heo

Levin

. Associations between patients’ understanding of periodontal disease, treatment compliance, and disease status. Quintessence Int. 2018;49(1):17-23. doi:10.3290/j.qi.a39096

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