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International Journal for Quality in Health Care logoLink to International Journal for Quality in Health Care
. 2025 Aug 7;37(3):mzaf075. doi: 10.1093/intqhc/mzaf075

Impact of early interruption from pay-for-performance program on progression and medical utilization for patients with early chronic kidney disease

Yeong-Ruey Chu 1,2, Liang-Yu Chiang 2,3,4,2, Pei-Tseng Kung 5,6, Wen-Chen Tsai 7,✉
Editor: Sonali Desai
PMCID: PMC12413898  PMID: 40795201

Abstract

Background

Most patients with chronic kidney disease (CKD) benefit from early intervention programs designed to slow disease progression and reduce medical usage. The Early-CKD Pay-for-Performance (Early-CKD P4P) program, implemented by Taiwan’s National Health Insurance Administration since 2011, has proven effective in this regard. However, there is limited research on outcomes for patients who interrupt their participation in the program. The study aimed to compare risks of emergency department visits, hospitalizations, and dialysis between those who continued versus those who interrupted the Early-CKD P4P program.

Methods

The study was a retrospective cohort study using nationwide data from Taiwan's National Health Insurance Research Database. We examined patients aged 18 and older who joined the Early-CKD P4P program between 2014 and 2016, with follow-up until the end of 2021. Propensity score matching ensured comparability between groups. Log-linear Poisson regression assessed emergency department visits and hospitalizations, while Cox proportional hazard models evaluated dialysis risk.

Results

A total of 100 228 participants joined the Early-CKD P4P program, with 37 457 in the continuous group and 62 771 in the interruption group, and 71 678 patients were matched. The patients who interrupted the program had significantly higher risks: emergency department visits were 3.41 times higher, hospitalizations 3.29 times higher, and the risk of dialysis was 5.46 times higher compared to the continuous group.

Conclusions

Premature interruption of the Early-CKD P4P program is associated with significantly increased risks of adverse outcomes. Sustained engagement in the program is crucial to reduce disease progression and healthcare utilization. These findings highlight the critical importance of ongoing participation in the Early-CKD P4P program to reduce adverse health outcomes. For the high rate of interruption observed in the study, future policies should focus on developing and implementing effective strategies to improve patient retention and maximize the benefits of this public health program.

Keywords: early chronic kidney disease, pay-for-performance, interruption

Introduction

In 2017, 690 million people worldwide had chronic kidney disease (CKD), with a prevalence of 9.1% [1]. The prevalence rates were ∼5% for Stages 1 and 2, 3.9% for Stage 3, and 0.07% for Stages 4 and 5 [1]. According to 2021 global survey results from the US Renal Data System, Taiwan had the second-highest incidence rate (529 cases per million population) and the highest prevalence rate (3679 cases per million population) of end-stage renal disease (ESRD) [2]. In 2021, CKD was among the top 10 most prevalent diseases in Taiwan, ranking ninth in mortality and having the highest medical expenses [3]. To reduce the risk of kidney disease progression, the government launched the ‘Early-CKD Pay-for-Performance Program (Early-CKD P4P program)’ in 2011, which classifies CKD into five stages based on the estimated glomerular filtration rate (eGFR). Stage 3 was further divided into 3a (eGFR 45–59 ml/min/1.73 m2) and 3b (eGFR 30–44 ml/min/1.73 m2) [4, 5]. The Early-CKD P4P program targeted patients with eGFR ≥45 ml/min/1.73 m2 (Stages 1–3a). For patients with eGFR <45 ml/min/1.73 m2 (Stages 3b–5), a separate pre-ESRD program was implemented for disease care [6].

The P4P encouraged medical providers to offer higher-quality services based on disease performance and outcomes [7, 8]. It focused on quality indicators of medical services to foster more efficient healthcare services. The P4P aimed to establish medical teams for long-term follow-ups, providing appropriate treatment and health education to prevent or slow kidney function deterioration [6]. Patients were followed every 6 months, with rewards provided for improvement in kidney disease stage or control of indicators such as kidney function, blood pressure, blood sugar, and blood lipids [6].

The participation rate of the Early-CKD P4P increased from 239 217 participants in 2014 to 386 663 in 2018, with the care rate rising from 36.5% to 39.9% [9]. Previous studies have shown that intervention through the Early-CKD P4P can reduce the kidney disease deterioration, risk of death, and medical utilization [10, 11]. One study indicated that patients enrolled in the Early-CKD P4P had higher numbers and costs of outpatient visits but lower emergency and hospitalization rates compared to non-enrolled patients [12]. This demonstrates that the Early-CKD P4P significantly improved continuous care indicators in patients with CKD. Other studies showed that patients who joined the P4P had a lower risk of progression to stage 3b kidney disease [10, 11]. A survey found that patients who participated in the Early-CKD P4P had an improved quality of life, self-management scores, and eGFR outcomes [13].

Previous studies primarily focused on comparing patients who joined the Early-CKD P4P program; patients who discontinued follow-up were usually excluded from the study, and the prognosis of these patients was not further explored. Therefore, this study aimed to investigate the impact on subsequent disease progression and medical utilization in patients who prematurely interrupted the Early-CKD P4P program, using nationwide data.

Methods

Data sources and participants

This study focused on CKD patients who joined the Early-CKD P4P program between 2014 and 2016 and were followed until 2021. Patients who discontinued within 3 years were placed in the interruption group, while those who continued were in the continuous group. Program closures due to normal kidney recovery, progression to stage 3b or higher, or death were also included in the continuous group. Excluded were patients under 18, who died within a year of joining, with catastrophic illness before joining, who discontinued after joining more than 3 years, and with incomplete data. To reduce group differences, 1:1 propensity score matching (PSM) was applied [14], matching variables such as CKD stage, gender, age, comorbidities, and medical institution characteristics. The flowchart for participant selection is shown in Fig. 1.

Figure 1.

Figure 1

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Flowchart of the process for selecting study subjects

Data were obtained from the National Health Insurance Research Database and the Chronic Kidney Disease Health Database (2012–21) from the Ministry of Health and Welfare [15]. The National Health Insurance Research Database, established in 1995, covers 99.93% of the population and has supported numerous studies [16]. The Chronic Kidney Disease Health Database includes data from patients in the Early-CKD P4P program, recording basic patient characteristics and health indicators like body mass index (BMI), smoking status, systolic blood pressure (SBP), eGFR, low-density lipoprotein (LDL), and glycated hemoglobin (HbA1c).

Variables description

This study examined patient participation in the Early-CKD P4P program as an independent variable. The dependent variables were the risk of emergency department (ED) visits, hospitalization, and dialysis due to CKD. Control variables included kidney disease stage, gender, age, monthly salary, urbanization of residence, CCI, diabetes, hypertension, hyperlipidemia, cardiovascular diseases, gout, BMI, smoking status, physiological indicators (SBP, LDL, HbA1c), and medical institution characteristics (institution level, ownership, urbanization, and consultation department). Kidney disease stages were divided based on eGFR: Stage 1 (GFR 90 or more ml/min/1.73 m2 with protein in urine), Stage 2 (GFR between 60 and 89 ml/min/1.73 m2), and Stage 3a (GFR between 45 and 59 ml/min/1.73 m2). Age was categorized as <45, 45–54, 55–64, 65–74, or ≥75 years, and salary as ≤NTD$20 008, NTD$20 009–28 800, NTD$28 801–36 300, NTD$36 301–45 800, or ≥NTD$45 801. Urbanization was categorized from highest level 1 to lowest level 7 [17].

Health conditions for disease history were defined as having at least three outpatient visits or one hospital admission within 2 years before joining the Early-CKD P4P program, based on the primary diagnosis codes in the International Classification of Diseases, Ninth Revision, Clinical Modification (ICD-9-CM) or the International Classification of Diseases, Tenth Revision, Clinical Modification (ICD-10-CM). Diabetes was defined using the ICD-9-CM code 250 or ICD-10-CM codes E08–E11 and E13. Using ICD-9-CM codes 401–405 or ICD-10-CM codes I10–I15, hypertension was indicated. Hyperlipidemia was represented by the ICD-9-CM code 272 or the ICD-10-CM code E78. Cardiovascular diseases included stroke (ICD-9-CM: 434–436, ICD-10-CM codes I60–I63, I65–I69), congestive heart disease (ICD-9-CM: 428, ICD-10-CM: I50), and ischemic heart disease (ICD-9-CM: 410–414, ICD-10-CM: I20–I25). Gout was defined as ICD-9-CM code 274 or ICD-10-CM code M10. The CCI severity was figured out by using a method from Deyo et al., which changed the ICD-9-CM and ICD-10-CM main diagnosis codes from 2 years before starting the Early-CKD P4P into numbers, leaving out the codes related to CKD, diabetes, hypertension, hyperlipidemia, cardiovascular diseases, and gout, and then adding these numbers together. Scores were categorized into 0, 1, 2, and ≥3 points [18, 19].

BMI was categorized based on the Health Promotion Administration of Taiwan’s standards: BMI < 18.5 kg/m2, 18.5 ≤ BMI < 24, 24 ≤ BMI < 27, and BMI ≥ 27 kg/m2 [20]. Physiological indicators were recorded at program entry. SBP was categorized into <90, 90–129, and ≥130 mmHg; LDL into <100, 100–129, and ≥130 mg/dl; and HbA1c into <7.0%, 7–9%, and >9%. Medical institutional characteristics included participation in the Early-CKD P4P. Primary healthcare organization levels were academic medical centers, regional hospitals, district hospitals, and clinics. Hospitals were categorized by ownership (public or nonpublic), and urbanization was classified into Levels 1–5 [17]. Consultation departments included nephrology, cardiology, metabolism, and family medicine.

Statistical analysis

Descriptive statistics were used to summarize the study variables, including kidney disease stages, basic characteristics, monthly salary, urbanization, health status, BMI, smoking, physiological indicators, and medical institution characteristics. To reduce selection bias, PSM matched the interruption and continuous groups in a 1:1 ratio based on the joining year and variables like kidney disease stage, basic characteristics, salary, urbanization, health status, BMI, and smoking. An standardized mean difference (SMD) ≤ 0.1 indicated no significant difference between the groups [21].

ED visits and hospitalizations due to CKD were presented as visits per 1000 person-years. A univariate Poisson regression described the interruption and continuous groups, and a log-linear Poisson regression analyzed visit and hospitalization risks between the groups, adjusting for kidney disease stage, basic characteristics, salary, urbanization, health status, BMI, smoking, and medical institution characteristics. Results were presented as adjusted relative risk (aRR) with 95% confidence intervals (CI). The log-rank test analyzed dialysis risk, and a Cox proportional hazard model compared dialysis risk between groups, considering death as a competing risk, and control variables were like those in the Poisson regression, with results presented as adjusted hazard ratios (aHR) and 95% CI. Cumulative incidence plots and Gray’s test compared dialysis rates between groups. Statistical analysis used SAS version 9.4, with a two-tailed P-value <0.05 considered significant.

Results

Characteristics of new joined Early-CKD P4P program patient from 2014 to 2016

Participants were followed until the end of 2021, with an average follow-up of 5.95 years (6.28 years for the continuous group, 5.63 years for the interruption group). Table 1 shows that from 2014 to 2016, 100 228 participants joined the Early-CKD P4P program, with 37 457 (37.37%) in the continuous group and 62 771 (62.63%) in the interruption group. Of all participants, Stage 3a had the most participants (42.60%). All variables showed significant differences with continuous participation, except for smoking (P = 0.263).

Table 1.

Characteristics of new joined Early-CKD P4P program patient from 2014 to 2016 (interrupted within 3 years)

Variable Total
 Continuous group
 Interruption group
 P-valuea
N % N % N %
Total 100 228 100.00 37 457 37.37 62 771 62.63
CKD stage <0.001
 1 23 169 23.12 9665 41.72 13 504 58.28
 2 34 357 34.28 14 754 42.94 19 603 57.06
 3a 42 702 42.60 13 038 30.53 29 664 69.47
Gender 0.004
 Male 53 125 53.00 19 621 36.93 33 504 63.07
 Female 47 103 47.00 17 836 37.87 29 267 62.13
Age <0.001
 <45 7259 7.24 2602 35.85 4657 64.15
 45–54 14 390 14.36 6129 42.59 8261 57.41
 55–64 28 060 28.00 12 417 44.25 15 643 55.75
 65–74 26 757 26.70 10 682 39.92 16 075 60.08
 ≥75 23 762 23.71 5627 23.68 18 135 76.32
Monthly salary (NTD) <0.001
 ≤20 008 27 856 27.79 9186 32.98 18 670 67.02
 20 009–28 800 43 176 43.08 16 070 37.22 27 106 62.78
 28 801–36 300 8414 8.39 3453 41.04 4961 58.96
 36 301–45 800 10 545 10.52 4583 43.46 5962 56.54
 >45 800 10 237 10.21 4165 40.69 6072 59.31
Urbanization of residence area <0.001
 1 20 370 20.32 7737 37.98 12 633 62.02
 2 30 541 30.47 11 578 37.91 18 963 62.09
 3 20 210 20.16 7612 37.66 12 598 62.34
 4 16 518 16.48 6295 38.11 10 223 61.89
 ≥5 12 589 12.56 4235 33.64 8354 66.36
CCI <0.001
 0 48 322 48.21 19 905 41.19 28 417 58.81
 1 23 748 23.69 9003 37.91 14 745 62.09
 2 16 537 16.50 5445 32.93 11 092 67.07
 ≥3 11 621 11.59 3104 26.71 8517 73.29
Diabetes <0.001
 No 9532 9.51 2063 21.64 7469 78.36
 Yes 90 696 90.49 35 394 39.02 55 302 60.98
Hypertension <0.001
 No 22 690 22.64 8865 39.07 13 825 60.93
 Yes 77 538 77.36 28 592 36.87 48 946 63.13
Hyperlipidemia <0.001
 No 25 247 25.19 7454 29.52 17 793 70.48
 Yes 74 981 74.81 30 003 40.01 44 978 59.99
Cardiovascular diseases <0.001
 No 65 215 65.07 26 812 41.11 38 403 58.89
 Yes 35 013 34.93 10 645 30.40 24 368 69.60
Gout <0.001
 No 83 287 83.10 31 857 38.25 51 430 61.75
 Yes 16 941 16.90 5600 33.06 11 341 66.94
BMI <0.001
 <18.5 1781 1.78 384 21.56 1397 78.44
 18.5–23.9 29 261 29.19 9635 32.93 19 626 67.07
 24–26.9 30 782 30.71 11 662 37.89 19 120 62.11
 ≥27 38 404 38.32 15 776 41.08 22 628 58.92
Smoking status 0.263
 No 89 140 88.94 33 303 37.36 55 837 62.64
 Yes 11 088 11.06 4154 37.46 6934 62.54
SBP <0.001
 <90 247 0.25 62 25.10 185 74.90
 90–130 42 966 42.87 15 939 37.10 27 027 62.90
 >130 57 015 56.89 21 456 37.63 35 559 62.37
LDL <0.001
 <100 52 455 52.34 21 032 40.10 31 423 59.90
 100–130 29 558 29.49 10 743 36.35 18 815 63.65
 >130 18 215 18.17 5682 31.19 12 533 68.81
HbA1c <0.001
 <7 45 710 45.61 15 918 34.82 29 792 65.18
 7–9 37 042 36.96 15 508 41.87 21 534 58.13
 >9 17 476 17.44 6031 34.51 11 445 65.49
Medical institutions <0.001
 Medical center 23 525 23.47 7677 32.63 15 848 67.37
 Regional hospital 33 448 33.37 11 631 34.77 21 817 65.23
 District hospital 18 902 18.86 6734 35.63 12 168 64.37
 Clinic 24 353 24.30 11 415 46.87 12 938 53.13
Institutional ownership <0.001
 Public 18 267 18.23 6165 33.75 12 102 66.25
 Nonpublic 81 961 81.77 31 292 38.18 50 669 61.82
Urbanization of medical institution <0.001
 1 31 066 31.00 10 882 35.03 20 184 64.97
 2 37 484 37.40 13 813 36.85 23 671 63.15
 3 11 252 11.23 4729 42.03 6523 57.97
 4 16 435 16.40 6616 40.26 9819 59.74
 ≥5 3991 3.98 1417 35.50 2574 64.50
Departments of consultation <0.001
 Nephrology 14 950 14.92 5065 33.88 9885 66.12
 Cardiology 14 036 14.00 3710 26.43 10 326 73.57
 Metabolism 32 649 32.57 14 272 43.71 18 377 56.29
 Family medicine 15 727 15.69 6572 41.79 9155 58.21
 Others 22 866 22.81 7838 34.28 15 028 65.72
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a

Chi-squared test.

Comparisons of study subjects after propensity score matching for Early-CKD P4P participating status

In Supplementary Appendix Table S1, to minimize selection bias between the continuous and interruption groups in subsequent analyses, the study matched participants in a 1:1 ratio using PSM. After matching, 35 839 participants were retained in each group, with all matching variable groups having an SMD ≤0.1.

Comparing the risk difference in ED visits and hospitalization due to CKD among patients in the Early-CKD P4P program

In the analysis of ED visit risk owing to CKD, differences were observed in risk between the continuous and interruption groups. As shown in Table 2 and Supplementary Appendix Table S2, the risk of ED visits due to CKD was 25.10 per 1000 person-years and 87.55 per 1000 person-years in the continuous and interruption groups, and Stage 3a had the highest ED visits (91.91 per 1000 person-years). After controlling for other factors using a log-linear Poisson regression model, the results showed that the interruption group had a higher risk of ED visits than the continuous group (aRR = 3.41, 95% CI: 3.20–3.65). In terms of stage, compared to Stage 1 as the reference group, Stage 2 and Stage 3a had significantly higher risks of ED visits.

Table 2.

Comparing the risk difference in ED visits and hospitalization due to CKD among patients in the Early-CKD P4P program

Variable ED visits
 Hospitalization
aRR 95% CI
 P-valuea aRR 95% CI
 P-valuea
Participating status
 Continuous group 1.00 1.00
 Interruption group 3.41 3.20 3.65 <0.001 3.29 3.11 3.49 <0.001
CKD stage
 1 1.00 1.00
 2 2.30 2.04 2.60 <0.001 2.05 1.80 2.35 <0.001
 3a 3.95 3.48 4.49 <0.001 3.49 3.06 4.00 <0.001
Gender
 Male 1.00 1.00
 Female 1.23 1.15 1.32 <0.001 1.20 1.13 1.28 <0.001
Age
 <45 1.00 1.00
 45–54 0.84 0.69 1.02 0.071 0.70 0.56 0.87 0.002
 55–64 0.75 0.63 0.91 0.002 0.67 0.54 0.83 <0.001
 65–74 0.63 0.52 0.75 <0.001 0.58 0.46 0.73 <0.001
 ≥75 0.84 0.70 1.02 0.077 0.78 0.62 1.00 0.047
Monthly salary (NTD)
 ≤20 008 1.00 1.00
 20 009–28 800 0.92 0.85 0.99 0.036 0.93 0.87 0.99 0.033
 28 801–36 300 0.76 0.67 0.85 <0.001 0.78 0.71 0.87 <0.001
 36 301–45 800 0.80 0.70 0.91 0.001 0.86 0.77 0.96 0.008
 >45 800 0.62 0.55 0.71 <0.001 0.76 0.65 0.88 <0.001
Urbanization of residence area
 1 1.00 1.00
 2 1.13 1.03 1.24 0.013 1.10 1.01 1.20 0.029
 3 1.23 1.10 1.37 <0.001 1.13 1.04 1.23 0.005
 4 1.30 1.14 1.47 <0.001 1.18 1.06 1.30 0.002
 ≥5 1.24 1.10 1.41 <0.001 1.24 1.11 1.37 <0.001
CCI
 0 1.00 1.00
 1 1.05 0.97 1.14 0.203 1.09 1.02 1.16 0.011
 2 1.30 1.19 1.43 <0.001 1.26 1.17 1.36 <0.001
 ≥3 1.72 1.56 1.90 <0.001 1.72 1.54 1.92 <0.001
Diabetes
 No 1.00 1.00
 Yes 1.52 1.35 1.71 <0.001 1.46 1.22 1.76 <0.001
Hypertension
 No 1.00 1.00
 Yes 1.50 1.35 1.65 <0.001 1.59 1.45 1.73 <0.001
Hyperlipidemia
 No 1.00 1.00
 Yes 0.91 0.84 0.98 0.011 0.87 0.81 0.94 <0.001
Cardiovascular diseases
 No 1.00 1.00
 Yes 1.39 1.30 1.49 <0.001 1.38 1.31 1.47 <0.001
Gout
 No 1.00 1.00
 Yes 1.05 0.97 1.13 0.236 1.06 0.99 1.13 0.119
BMI
 <18.5 1.56 1.21 2.00 <0.001 1.35 1.10 1.65 0.003
 18.5–23.9 1.00 1.00
 24–26.9 0.80 0.74 0.87 <0.001 0.81 0.75 0.87 <0.001
 ≥27 0.75 0.69 0.82 <0.001 0.75 0.69 0.81 <0.001
Smoking status
 No 1.00 1.00
 Yes 1.11 1.01 1.23 0.036 1.13 1.04 1.23 0.004
Medical institutions
 Medical center 1.00 1.00
 Regional hospital 0.86 0.78 0.93 <0.001 0.94 0.87 1.01 0.099
 District hospital 0.75 0.68 0.83 <0.001 0.87 0.80 0.95 0.002
 Clinic 0.57 0.51 0.65 <0.001 0.64 0.57 0.73 <0.001
Institutional ownership
 Public 1.00 1.00
 Nonpublic 0.80 0.74 0.87 <0.001 0.93 0.86 0.99 0.030
Urbanization of medical institution
 1 1.00 1.00
 2 1.17 1.07 1.27 <0.001 1.04 0.97 1.12 0.222
 3 0.94 0.83 1.06 0.308 0.95 0.85 1.06 0.343
 4 1.21 1.08 1.36 0.001 1.09 0.99 1.19 0.083
 ≥5 0.97 0.81 1.16 0.748 0.89 0.77 1.04 0.137
Departments of consultation
 Nephrology 1.00 1.00
 Cardiology 0.63 0.56 0.71 <0.001 0.65 0.58 0.71 <0.001
 Metabolism 0.84 0.76 0.93 0.001 0.85 0.78 0.93 <0.001
 Family medicine 0.59 0.52 0.67 <0.001 0.63 0.57 0.70 <0.001
 Others 0.70 0.63 0.78 <0.001 0.71 0.64 0.79 <0.001
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a

Log-linear Poisson regression model.

Table 2 and Supplementary Appendix Table S3 present the results of the risk of hospitalization due to CKD, showing that the continuous group had 33.95 visits per 1000 person-years, whereas the interruption group had 114.00 visits per 1000 person-years. In terms of stage, Stage 3a had the highest visits (118.84 per 1000 person-years). After using the log-linear Poisson regression model with controlling for other factors, patients in the interruption group had a significantly higher risk of hospitalization due to CKD than the continuous group (aRR = 3.29, 95% CI: 3.11–3.49). In terms of stage, compared to Stage 1 patients, Stages 2 and 3a had significantly higher risks of hospitalization.

Comparing the risk of dialysis among patients in the Early-CKD P4P program

Table 3 shows that 276 (0.77%) patients in the continuous group and 1407 (3.93%) patients in the interruption group underwent dialysis. Analysis using a Cox proportional hazard model, considering death as a competing risk and controlling for related factors, showed that the interruption group had a significantly higher risk of dialysis than the continuous group (aHR = 5.46, 95% CI: 4.65–6.41). Regarding CKD staging, with Stage 1 as the reference group, higher stages were associated with increased risk of dialysis.

Table 3.

Comparing the risk of dialysis among patients in the Early-CKD P4P program

Variable Dialysis
 P-valuea aHR 95% CI
 P-valueb
Total
 Yes
 No
N % N % N %
Total 71 678 100.00 1683 2% 69 995 98%
Participating status <0.001
 Continuous group 35 839 50.00 276 0.77 35 563 99.23 1.00
 Interruption group 35 839 50.00 1407 3.93 34 432 96.07 5.46 4.65 6.41 <0.001
CKD stage <0.001
 1 18 258 25.47 187 1.02 18 071 98.98 1.00
 2 27 262 38.03 617 2.26 26 645 97.74 2.53 1.74 3.68 <0.001
 3a 26 158 36.49 879 3.36 25 279 96.64 10.12 5.78 17.73 <0.001
Gender <0.001
 Male 37 594 52.45 1041 2.77 36 553 97.23 1.00
 Female 34 084 47.55 642 1.88 33 442 98.12 1.22 0.93 1.59 0.157
Age 0.002
 <45 5099 7.11 196 3.84 4903 96.16 1.00
 45–54 11 580 16.16 412 3.56 11 168 96.44 0.40 0.19 0.81 0.012
 55–64 23 003 32.09 637 2.77 22 366 97.23 0.23 0.10 0.56 0.001
 65–74 20 593 28.73 339 1.65 20 254 98.35 0.11 0.05 0.24 <0.001
 ≥75 11 403 15.91 99 0.87 11 304 99.13 0.17 0.07 0.39 <0.001
Monthly salary (NTD) <0.001
 ≤20 008 18 339 25.59 460 2.51 17 879 97.49 1.00
 20 009–28 800 30 904 43.12 708 2.29 30 196 97.71 0.76 0.48 1.18 0.214
 28 801–36 300 6393 8.92 147 2.30 6246 97.70 0.62 0.35 1.09 0.094
 36 301–45 800 8314 11.60 198 2.38 8116 97.62 0.51 0.28 0.92 0.024
 >45 800 7728 10.78 170 2.20 7558 97.80 0.51 0.28 0.94 0.030
Urbanization of residence area 0.074
 1 14 803 20.65 329 2.22 14 474 97.78 1.00
 2 22 227 31.01 553 2.49 21 674 97.51 1.12 0.81 1.56 0.502
 3 14 431 20.13 318 2.20 14 113 97.80 1.23 0.81 1.88 0.337
 4 11 837 16.51 285 2.41 11 552 97.59 1.31 0.87 1.99 0.197
 ≥5 8380 11.69 198 2.36 8182 97.64 1.22 0.67 2.21 0.509
CCI <0.001
 0 37 913 52.89 944 2.49 36 969 97.51 1.00
 1 17 010 23.73 325 1.91 16 685 98.09 1.02 0.74 1.40 0.924
 2 10 642 14.85 260 2.44 10 382 97.56 2.04 1.41 2.96 <0.001
 ≥3 6113 8.53 154 2.52 5959 97.48 1.64 0.86 3.13 0.137
Diabetes <0.001
 No 4085 5.70 29 0.71 4056 99.29 1.00
 Yes 67 593 94.30 1654 2.45 65 939 97.55 1.31 0.42 4.09 0.648
Hypertension <0.001
 No 16 715 23.32 312 1.87 16 403 98.13 1.00
 Yes 54 963 76.68 1371 2.49 53 592 97.51 1.78 1.32 2.40 <0.001
Hyperlipidemia 0.796
 No 14 579 20.34 287 1.97 14 292 98.03 1.00
 Yes 57 099 79.66 1396 2.44 55 703 97.56 0.78 0.44 1.37 0.382
Cardiovascular diseases <0.001
 No 50 593 70.58 1253 2.48 49 340 97.52 1.00
 Yes 21 085 29.42 430 2.04 20 655 97.96 1.79 0.97 3.30 0.061
Gout 0.058
 No 60 839 84.88 1418 2.33 59 421 97.67 1.00
 Yes 10 839 15.12 265 2.44 10 574 97.56 0.81 0.54 1.21 0.295
BMI 0.006
 <18.5 756 1.05 21 2.78 735 97.22 9.62 3.38 27.34 <0.001
 18.5–23.9 19 028 26.55 509 2.68 18 519 97.32 1.00
 24–26.9 22 342 31.17 513 2.30 21 829 97.70 0.76 0.53 1.10 0.141
 ≥27 29 552 41.23 640 2.17 28 912 97.83 0.46 0.29 0.73 0.001
Smoking status <0.001
 No 63 805 89.02 1434 2.25 62 371 97.75 1.00
 Yes 7873 10.98 249 3.16 7624 96.84 1.14 0.74 1.77 0.549
SBP <0.001
 <90 142 0.20 6 4.23 136 95.77 70.03 12.40 395.44 <0.001
 90–130 30 189 42.12 501 1.66 29 688 98.34 1.00
 >130 41 347 57.68 1176 2.84 40 171 97.16 1.38 1.13 1.69 0.002
LDL <0.001
 <100 37 771 52.70 721 1.91 37 050 98.09 1.00
 100–130 20 984 29.28 475 2.26 20 509 97.74 0.99 0.77 1.26 0.925
 >130 12 923 18.03 487 3.77 12 436 96.23 1.41 1.11 1.79 0.005
HbA1c <0.001
 <7 30 086 41.97 359 1.19 29 727 98.81 1.00
 7–9 28 286 39.46 598 2.11 27 688 97.89 2.08 1.63 2.65 <0.001
 >9 13 306 18.56 726 5.46 12 580 94.54 4.86 3.69 6.38 <0.001
Medical institutions <0.001
 Medical center 15 912 22.20 501 3.15 15 411 96.85 1.00
 Regional hospital 23 379 32.62 637 2.72 22 742 97.28 0.97 0.75 1.26 0.824
 District hospital 13 263 18.50 280 2.11 12 983 97.89 0.99 0.70 1.40 0.938
 Clinic 19 124 26.68 265 1.39 18 859 98.61 0.64 0.45 0.89 0.008
Institutional ownership 0.009
 Public 12 393 17.29 326 2.63 12 067 97.37 1.00
 Nonpublic 59 285 82.71 1357 2.29 57 928 97.71 0.66 0.48 0.90 0.008
Urbanization of medical institution <0.001
 1 21 776 30.38 562 2.58 21 214 97.42 1.00
 2 26 691 37.24 653 2.45 26 038 97.55 0.62 0.47 0.81 <0.001
 3 8486 11.84 173 2.04 8313 97.96 0.71 0.49 1.04 0.080
 4 11 886 16.58 253 2.13 11 633 97.87 0.60 0.41 0.87 0.007
 ≥5 2839 3.96 42 1.48 2797 98.52 0.63 0.34 1.16 0.135
Departments of consultation <0.001
 Nephrology 9658 13.47 271 2.81 9387 97.19 1.00
 Cardiology 8656 12.08 146 1.69 8510 98.31 0.59 0.39 0.90 0.015
 Metabolism 25 348 35.36 754 2.97 24 594 97.03 0.87 0.64 1.18 0.356
 Family medicine 11 986 16.72 194 1.62 11 792 98.38 0.71 0.48 1.06 0.090
 Others 16 030 22.36 318 1.98 15 712 98.02 0.75 0.52 1.08 0.123
Open in a new tab
a

Log-rank test.

b

Cox proportional hazards model.

Furthermore, cumulative incidence plots for dialysis stratified by CKD stage were created to compare the continuous and interruption groups (Fig. 2). The analysis confirmed that within each stage, the interruption group had a significantly higher cumulative incidence of dialysis than the continuous group (P < 0.001).

Figure 2.

Figure 2

Open in a new tab

Cumulative incidence of dialysis for all patients and stratified by stage, comparing the interruption and continuous groups

Comparing the risk difference in ED visits, hospitalization, and dialysis among patients in the Early-CKD P4P program in different CKD stage

Supplementary Appendix Table S4 shows that after controlling for other factors, the interruption group had a significantly higher risk of adverse outcomes than the continuous group across all stages. The stage-stratified analysis revealed that this increased relative risk for the interruption group was most evident for ED visits in Stage 2 (aRR = 4.03, 95% CI: 3.58–4.53) and for hospitalization in Stage 1 (aRR = 4.05, 95% CI: 3.23–5.08). Regarding dialysis, the relative risk was markedly high for patients in Stage 1 (aHR = 7.46, 95% CI: 4.65–11.98).

Discussion

Statement of principal findings

This study focused on comparing differences between the ‘continuous group’ and the ‘interruption group’, directly addressing the common clinical compliance problem. The study revealed that 62.63% of those who joined in the Early-CKD P4P program between 2014 and 2016 discontinued follow-up, which poses a challenge in long-term CKD management.

After matching, patients who interrupted the program had significantly higher risks of dialysis, ED visits, and hospitalizations compared to the continuous group. This indicates that interrupting the P4P program leads to poorer outcomes.

We conducted a further stratified analysis by CKD stage to explore the distinctions between the interruption and continuous groups. The results revealed that across all CKD stages, the interruption group consistently had higher risks than the continuous group. However, a critical insight emerged that the relative risk increase was most evident for patients with early-­stage disease (Stage 1 and Stage 2). This finding is significant because these patients, often being asymptomatic, are most likely to be overlooked.

These findings emphasize the importance of continuity of care and further follow-up in the interruption group.

Strengths and limitations

This study has notable strengths. First, it utilized a large, nationwide database, enhancing the generalizability and reliability of the findings. Second, the study conducted a comprehensive assessment of hospitalization, ED visits, and dialysis risks across different stages of CKD, which revealed how program interruption affects patients at various stages. Third, by comparing interruption and continuous groups, the study highlights the importance of adherence to the Early-CKD P4P program in improving patient outcomes.

However, several limitations should be acknowledged. The database did not contain information on educational levels, limiting the ability to explore the impact of education on long-term engagement. Additionally, aside from smoking behavior, the database lacked data on other potentially influential health behaviors, such as dietary habits and alcohol consumption, preventing a more comprehensive analysis of lifestyle factors associated with disease progression. A further limitation pertains to the study’s exclusion criteria. Specifically, a substantial number of patients who discontinued the program more than 3 years after enrollment were excluded from the primary analysis. This decision was methodologically necessary to maintain a clear definition of early interruption. Similarly, patients who repeatedly joined and withdrew from the program were also excluded, as their unstable participation pattern makes it difficult to define a clear index date and assign them to either the continuous or interruption group. While excluding these complex cases was necessary for analytical clarity, it means our findings may not be fully generalizable to all patient adherence behaviors. These excluded groups may represent distinct cohorts with potentially different characteristics and outcomes. Future research is needed to explore these patient groups and provide a more comprehensive understanding of long-term participation.

Another limitation is the inability to analyze specific causes of death, as our database lacks this information. However, for the analysis of renal progression, our study used the initiation of dialysis as the primary outcome and appropriately accounted for death as a competing risk. Future research linking healthcare databases with the national death registry is essential to investigate this critical issue.

Interpretation within the context of the wider literature

The National Kidney Foundation and the US Food and Drug Administration have emphasized the critical role of early CKD to prevent disease progression [22]. Accurate CKD staging and timely referrals are key for reducing the global CKD burden [23], while effective management reduces both renal and cardiovascular risks, optimizing healthcare resources [24–26]. Since the Early-CKD P4P program launch in 2011, studies have demonstrated its significant impact on slowing the progression of kidney disease [10–12]. However, our study reveals a high real-world interruption rate. This finding alone represents a valuable contribution, highlighting a challenge that previous efficacy-focused studies have not thoroughly explored.

Prior research often excluded patients who discontinued the program. These patients, initially identified as requiring continuous care, represent an important group for understanding the full impact of program adherence. Our findings show that sustained participation improves clinical outcomes. Studies confirm that continuous P4P intervention is associated with better outcomes and reduced healthcare utilization [10–12, 27]. While our general conclusions are consistent with the existing literature that P4P programs benefit patients in disease control, our study adopts a different methodology that provides several unique insights. Whereas prior studies have typically compared patients participating in the P4P program with nonparticipants and often excluded those who discontinued care, our study focuses on comparing continuous participants with patients who had early interruptions. This approach directly addresses the critical question of long-term adherence.

Additionally, the stage-stratified analysis of relative risk revealed a critical insight. While the interruption group consistently had higher risks across all stages, the increase in relative risk was most pronounced in patients with early-stage disease (Stages 1 and 2). This suggests that the protective effect of continuous care is particularly important for the earliest-stage, often asymptomatic patients, whose risk of progression should not be underestimated. Given that early-stage CKD is often asymptomatic, improving patient awareness and education remains a crucial aspect of effective management [28, 29].

Implications for policy, practice, and research

Given the high program interruption rates found in this study, policy efforts must evolve from simply promoting program enrollment to actively ensuring long-term patient adherence and engagement. Policymakers should consider reviewing and adapting the P4P program’s incentive structure to reward healthcare providers not only for meeting clinical targets but also for maintaining high patient retention rates. Tailored interventions for high-risk populations, such as those with diabetes, hypertension, and cardiovascular diseases, could be particularly beneficial. Additionally, improving patient education to raise awareness of early CKD symptoms and the importance of regular follow-ups is crucial.

For clinicians and healthcare systems, improving patient awareness of their disease plays a key role in reducing program interruption rates. The focus should shift toward promoting patient self-management through shared decision-making and enhanced health literacy, particularly to help patients with early-stage, asymptomatic CKD understand the benefits of continuous monitoring. Long-term studies can help identify factors that improve program retention and reduce disease progression in early-stage CKD patients.

Strengthening the case management system can be a key strategy to address the high interruption rate found in this study. Case managers can serve as a dedicated point of contact, building trust and actively engaging with patients who have discontinued care to assist with follow-up visits. Furthermore, they can help coordinate appointments across different specialties and strengthen health education. This supportive mechanism is crucial for tracking high-risk patients and re-engaging them in continuous care, ultimately reducing the risks associated with program interruption.

Regarding the issue of missing values, those data are primarily required for patient follow-up under the P4P. The incomplete data collection might impact the overall implementation of the policy or its effectiveness in tracking, which warrants further investigation.

Conclusion

This study discovered that patients who join the Early-CKD P4P program had a high probability of discontinued follow-up, highlighting that program interruption represents a critical challenge in long-term CKD management.

Patients who interrupted the Early-CKD P4P program had significantly higher risks of ED visits, hospitalization, and dialysis than those who continued. Cumulative incidence analysis further confirmed these trends. In summary, the interruption from the Early-CKD P4P program is significantly associated with adverse health outcomes, underscoring the critical importance of sustained engagement in slowing disease progression and reducing healthcare utilization.

These findings reinforce the importance of sustained engagement in the Early-CKD P4P program to reduce disease progression and healthcare utilization. Future policies should focus on improving patient retention through enhanced follow-up and support strategies.

Supplementary Material

mzaf075_Supplementary_Data
mzaf075_supplementary_data.docx (78.9KB, docx)

Acknowledgements

We are grateful for use of the National Health Insurance Research Database and the Chronic Kidney Disease Health Database provided by the Health Insurance Information Integration Application Service Center, the National Health Insurance Administration, the Ministry of Health and Welfare, Taiwan.

Contributor Information

Yeong-Ruey Chu, Department of Health Services Administration, China Medical University, No.100, Section 1, Jingmao Road, Beitun District, Taichung, 406040, Taiwan.

Liang-Yu Chiang, Department of Public Health, China Medical University, No. 100, Section 1, Jingmao Road, Beitun District, Taichung, 406040, Taiwan; Department of Orthopedic Surgery, Taichung Armed Forces General Hospital, No.348, Sec.2, Chungshan Rd., Taiping Dist., Taichung, 411228, Taiwan; School of Medicine, National Defense Medical University, No.161, Sec. 6, Minquan E. Rd., Neihu Dist., Taipei, 11490, Taiwan.

Pei-Tseng Kung, Department of Healthcare Administration, Asia University, 500, Lioufeng Rd., Wufeng, Taichung, 413305, Taiwan; Department of Medical Research, China Medical University Hospital, China Medical University, No. 2, Yude Road, North District, Taichung, 40447, Taiwan.

Wen-Chen Tsai, Department of Health Services Administration, China Medical University, No.100, Section 1, Jingmao Road, Beitun District, Taichung, 406040, Taiwan.

Author contributions

Yeong-Ruey Chu (Study conception and design, Data collection, Data analysis and interpretation, Manuscript writing), Liang-Yu Chiang (Study conception and design, Data collection, Data analysis and interpretation, Manuscript writing), Pei-Tseng Kung (Data collection, Manuscript writing), and Wen-Chen Tsai (Study conception and design, Data collection, Data analysis and interpretation, Manuscript writing). All authors critically reviewed and approved the final manuscript.

Supplementary data

Supplementary data is available at IJQHC online.

Conflict of interest: All authors do not have any conflict of interest to disclose.

Funding

The study was supported by the grant CMU113-ASIA-03 from the China Medical University and Asia University.

Data availability

Regarding the data availability, data were obtained from the National Health Insurance Research Database published by the Ministry of Health and Welfare, Taiwan. Due to legal restrictions imposed by the Taiwan government related to the Personal Information Protection Act, the database cannot be made publicly available. All researchers can apply for using the databases to conduct their studies in the Science Center of the Ministry of Health and Welfare. Any raw data are not allowed to be brought out from the Health and Welfare Data Science Center. The restrictions prohibited the authors from making the minimal data set publicly available.

Ethics and other permissions

Data are available from the Health and Welfare Data Science Center of the Ministry of Health and Welfare (MOHW), Taiwan. This study obtained the databases published and managed by the MOHW. All researchers are allowed to use the databases for their interested studies. Before using the databases for research, all studies should get the IRB permission. The institutional review board of China Medical University Hospital approved this study (IRB No.: CMUH112-REC2-019).

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Associated Data

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

Supplementary Materials

mzaf075_Supplementary_Data
mzaf075_supplementary_data.docx (78.9KB, docx)

Data Availability Statement

Regarding the data availability, data were obtained from the National Health Insurance Research Database published by the Ministry of Health and Welfare, Taiwan. Due to legal restrictions imposed by the Taiwan government related to the Personal Information Protection Act, the database cannot be made publicly available. All researchers can apply for using the databases to conduct their studies in the Science Center of the Ministry of Health and Welfare. Any raw data are not allowed to be brought out from the Health and Welfare Data Science Center. The restrictions prohibited the authors from making the minimal data set publicly available.

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