Application of an interrupted time series analysis (ITS) to evaluate the effect of universal dialysis policy from 2006 to 2016 in a province of northeastern Thailand

Piyalak Pukdeesamai; Pongdech Sarakarn; Sirirat Anutrakulchai

doi:10.12688/f1000research.128094.1

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Research Article

Application of an interrupted time series analysis (ITS) to evaluate the effect of universal dialysis policy from 2006 to 2016 in a province of northeastern Thailand

[version 1; peer review: 1 approved with reservations]

Piyalak Pukdeesamai^1,2, Pongdech Sarakarn ^1,2, Sirirat Anutrakulchai³

PUBLISHED 21 Apr 2023

Author details Author details

¹ Epidemiology and Biostatistics Department, Faculty of Public Health, Khon Kaen University, Khon Kaen, 40000, Thailand
² ASEAN Cancer Epidemiology and Prevention Research Group, Khon Kaen University, Khon Kaen, 40000, Thailand
³ Division of Nephrology, Department of Internal Medicine Faculty, of Medicine, Khon Kaen University, Khon Kaen, 40000, Thailand

Piyalak Pukdeesamai
Roles: Conceptualization, Data Curation, Formal Analysis, Investigation, Methodology, Project Administration, Resources, Software, Validation, Visualization, Writing – Original Draft Preparation, Writing – Review & Editing

Pongdech Sarakarn
Roles: Conceptualization, Investigation, Methodology, Project Administration, Software, Supervision, Writing – Original Draft Preparation, Writing – Review & Editing

Sirirat Anutrakulchai
Roles: Investigation, Writing – Original Draft Preparation, Writing – Review & Editing

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Abstract

Background: An interrupted time series (ITS) analysis is a powerful tool for policy evaluation. In Thailand, chronic kidney disease (CKD) is a public health problem that requires a long recovery time and has a high treatment cost. The universal coverage policy for renal replacement therapy (universal dialysis policy), is used to treat this disease but policy evaluation using ITS analysis has rarely been conducted. This study applied ITS analysis to test the effect of such a policy between 2006 and 2016.
Methods: Data were retrieved from the electronic database of the health data center in Roi Et Province for the period between January 1, 2006 and December 31, 2016. 15,681 CKD stage 5 patients were included. The intervention under assessment was the universal health coverage system, which has been implemented since 2008.
Results: Results showed that before implementation of the universal dialysis policy, the overall trend of access to renal replacement therapy (RRT) slightly increased (0.74; 95% confidence interval (CI): 0.58, 0.90). After implementation of the policy, access sharply increased (6.10; 95%CI: 3.67, 8.54), while the linear trend after policy implementation also slightly increased (0.29; 95%CI: 0.05, 0.14). The stratified analysis showed the same linear directional trend before and immediately after implementing the universal dialysis policy.
Conclusions: Implementation of the universal dialysis policy positively impacted the rate of renal replacement therapy in CKD stage 5 patients, while access to health care services also increased.

Keywords

Chronic kidney disease, Universal health coverage, Interrupted time series

Corresponding author: Pongdech Sarakarn

Competing interests: No competing interests were disclosed.

Grant information: The author(s) declared that no grants were involved in supporting this work.

Copyright: © 2023 Pukdeesamai P et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

How to cite: Pukdeesamai P, Sarakarn P and Anutrakulchai S. Application of an interrupted time series analysis (ITS) to evaluate the effect of universal dialysis policy from 2006 to 2016 in a province of northeastern Thailand [version 1; peer review: 1 approved with reservations]. F1000Research 2023, 12:434 (https://doi.org/10.12688/f1000research.128094.1) First published: 21 Apr 2023, 12:434 (https://doi.org/10.12688/f1000research.128094.1) Latest published: 21 Apr 2023, 12:434 (https://doi.org/10.12688/f1000research.128094.1)

Introduction

Chronic kidney disease (CKD) is a major global public health problem. In high-income countries, the prevalence of CKD stages 1-5 in individuals aged ≥20 years is 8.6% in men and 9.6% in women, and in low- and middle-income countries it is 10.6% and 12.5% in men and women respectively.¹ In Asia, the prevalence of CKD stages 3-5 is 11.2%.² CKD has been linked to morbidity and mortality, for example with cardiovascular disease and anemia.³^,⁴ For CKD stage 3-5 patients, renal replacement therapy is considered to be a clinical practice that can consist of the modalities of hemodialysis, peritoneal dialysis, and kidney transplant. In Thailand from 2007 to 2011, the rate of hemodialysis decreased from 79.2% to 54.3%; the rate of peritoneal dialysis increased from 12.2% to 42.9%; while the rate of kidney transplantation decreased from 8.6% to 2.8%.⁵ Thailand implemented the universal dialysis policy for helping CKD patients to achieve health care treatment in 2008. Since then, there have been several studies related to CKD in Thailand, for example, an epidemiology study of CKD,⁵ and analyses looking at the prevalence or trend of glomerular filtration rate (GFR) measurement,⁶ chronic kidney disease prevention and reduction,⁶ survival rates and related factors for peritoneal dialysis (PD),⁷^–¹⁰ and national peritoneal dialysis.¹¹ However, there has rarely found a study that investigated the effect of the universal treatment policy on the rate of access to health care.

The study design that we used to evaluate the public health policy/intervention in Thailand was an interrupted time series (ITS).¹² Several studies have used an ITS to evaluate policies for non-communicable diseases. Few reviews of ITS analysis have been found in regard to policy evaluation concerning CKD.¹³^–¹⁵ Therefore, the aim of this study was to use an ITS analysis to compare the rate of renal replacement therapy in a province of Northern Thailand before and after the universal coverage of renal replacement therapy was implemented in January, 2008.

Methods

Data sources

A secondary data analysis was used in this study. As a first step we retrieved the data for the period between January 1, 2006 and December 31, 2016 from the Health Data Center (HDC) in Roi Et Province. HDC is a health database system that stores monthly health data since 2007 from all health care services belonging to the Ministry of Public Health, Thailand. The health data comprised demographic data, household data, community (village, activity), health facilities in the community, disability details, provider, service, outpatient department (diagnostic date, procedure, drug and laboratory), inpatient department (admission date, diagnostic date, procedure, drug and laboratory) and the prevalence of communicable diseases. The data does not contain identifying information and cannot be linked to individuals. We selected participants with an estimated GFR (eGFR) of less than 15 ml/min/1.73m² (CKD stage 5), aged ≥18 years and with universal health coverage were included in our study.

Ethical considerations and consent

The Khon Kaen University Ethics Committee for Human Research approved this study (permit no.: HE642084).

Study variables

The rate of access to renal replacement therapy (RRT) services was calculated by the number of participants accessing treatment for RRT (peritoneal dialysis, hemodialysis) among all dialysis modalities. This data does not include kidney transplant patients. We aggregated the data for each month during the study period. Therefore, the data were created for120 monthly study points between January 1, 2006 and December 31, 2016.

We also collected information including gender (male, female), age (18 to 44 years, 45 to 60 years, > 60 years), and the primary cause of renal failure (hypertension, diabetes, heart disease).

Data analysis

In the ITS analysis, segmented regression was used to test the effect of implementation before, and immediately after the intervention. The strength of the model distinguished the effect of the intervention from secular change. Time series data often have many issues that may affect to the robustness of the analysis such as seasonality, time-varying confounders, use of control, and for other more complex ITS designs, over-dispersion and autocorrelation.¹² Seasonality refers to some events occurring more frequently than others, which may affect to the results. Time-varying confounders may occur because of the longitudinal data and age variation across time. Finally, autocorrelation is the correlation between the values of the same variables across different observations (period of the study).¹⁶

The dataset for the segmented regression analysis comprised the rate for the event and the time point when the intervention was implemented. An interrupted time series analysis measures trends as slope changes before and after implementation of the intervention, and also the effect seen when the intervention is implemented (the instant effect). Our analysis constructed a series of quarterly rates of access to treatment for renal replacement therapy for CKD stage 5 patients from January 2006 to December 2016. The 24-month pre-intervention period was from January 2006 to December 2007, while the 96-month post-intervention period took place from January 2008 to December 2016. We aggregated the data with 3-month interval. Therefore, there have an 8-time points in the pre-intervention period and 32-time point in post-intervention.

The equation for the ITS analysis¹⁷^,¹⁸ was as follows:

Y = β_{0} + β_{1} \times time before intervention (x 1) + β_{2} \times intervention (x 2) + β_{3} \times time after intervention + e_{t}

where Y is the rate of access to renal replacement therapy treatment,

β_{0}

is a constant term, and

β_{1}

is the coefficient of the time before intervention. Regarding the trend of access to the clinic before implementing the policy,

β_{2}

is the coefficient of the intervention immediately (level change), and

β_{3}

is the coefficient of time after intervention that refers to the effects of the intervention over time (a different slope before and after intervention). For autocorrelation, we used robustness to fit the model. Stata software, version 12.0, was used to perform all the analyses.

Results

Table 1 shows the characteristics of participants. We included 15,681 patients in the study, which was a total of 1,844 patients before policy (BP) implementation, and 13,837 patients after policy (AP) implementation. 11.76% of the participants were BP and 88.24% of the participants were AP. Most were aged over 60 years and had hypertension conditions, both BP and AP. We also described the rate of treatment for peritoneal dialysis (PD) and hemodialysis that was shown in Table 2.

Table 1. Description of chronic kidney disease (CKD) stage-5 patients, before and after policy implementation.

Variables	Total	Before policy	%	After policy	%
All	15,681	1,844	11.76	13,837	88.24
Sex
Male	7,711	808	10.48	6,903	89.52
Female	7,970	1,036	13.00	6,934	87.00
Age
18-44	1,704	249	14.61	1,455	85.39
45-60	3,903	479	12.27	3,424	87.73
>60	10,074	1,116	11.08	8,958	88.92
Primary cause of renal failure
Hypertension	10,028	1,030	10.27	8,998	89.73
Diabetes	6,224	584	9.38	5,640	90.62
Heart Disease	3,160	386	12.22	2,774	87.78
Renal replacement therapy
Yes	2,332	47	2.02	2,285	97.98
Peritoneal dialysis: PD	258	-	-	258	100.00
Hemodialysis: HD	2,074	47	2.27	2,027	97.73
No	13,349	1,797	13.46	11,552	86.54

Table 2. The rate of patients who have had renal replacement therapy (RRT) by sex, age, and primary cause.

Variables	Total	Before policy	%	After policy	%
Sex
Male	1,048	22	2.10	1026	97.90
Female	1,026	25	2.44	1,001	97.56
Age
18-44	337	16	4.75	321	95.25
45-60	818	15	1.83	803	98.17
>60	919	16	1.74	903	98.26
Primary cause of renal failure
Hypertension	1,864	39	2.09	1,825	97.91
Diabetes	1,337	20	1.50	1,317	98.50
Heart disease	613	8	1.31	605	98.69

Table 3 shows that the overall trend of access to RRT increased before policy implementation by 0.74 (95%CI: 0.58, 0.90), while after policy implementation the trend sharply increased by 6.10 (95%CI: 3.67, 8.54). The linear trend after policy implementation slightly increased by 0.29 (95%CI: 0.17, 0.41). The overall trend is shown in Figure 1. For those aged 60 or over, the trend slightly increased before policy implementation, while immediately after policy implementation the trend sharply increased by 5.26 (95%CI: 3.50, 7.01). For those whose primary cause of renal failure was diabetes, hypertension, and heart disease, the trend after policy implementation sharply increased by 9.11 (95%CI: 5.25,12.96), 7.65 (95%CI: 2.22, 13.07), and 11.83 (95%CI: 5.44, 18.22) respectively.

Table 3. Results from the interrupted time series (ITS) on the rate of access to renal replacement therapy before and after policy implementation.

	Trend before policy		Change in level after policy		Linear trend after policy
	β (95% CI)	P value	β (95% CI)	P value	β (95% CI)	P value
All	0.74 (0.58, 0.90)	<0.001	6.10 (3.67, 8.54)	<0.001	0.29 (0.17, 0.41)	<0.001
Sex
Male	0.79 (0.52, 1.07)	<0.001	5.69 (2.34, 9.03)	0.001	0.29 (0.14, 0.44)	0.0004
Female	0.70 (0.55,0.84)	<0.001	6.72 (4.46, 8.98)	<0.001	0.27 (0.15, 0.38)	<0.001
Age
18-44	2.31 (1.07, 3.56)	0.001	2.93 (-6.13, 12.00)	0.517	0.20 (0.047, 0.35)	0.0119
45-60	1.15 (0.76, 1.55)	<0.001	9.05 (5.08, 13.02)	<0.001	0.52 (0.36, 0.69)	<0.001
>60	0.40 (0.32, 0.49)	<0.001	5.26 (3.50, 7.01)	<0.001	0.18 (0.10, 0.26)	0.0001
Primary cause of renal failure
Diabetes
Yes	1.50 (1.21, 1.79)	<0.001	9.11 (5.25, 12.96)	<0.001	0.37 (0.19, 0.56)	0.0002
No	0.50 (0.08, 0.92)	0.02	2.93 (-0.59, 6.47)	0.101	0.15 (0.06, 0.23)	0.0012
Hypertension
Yes	1.28 (0.73, 1.85)	<0.001	7.65 (2.22, 13.07)	0.007	0.32 (0.07, 0.18)	0.0001
No	0.09 (-0.29, 0.46)	0.643	1.95 (-0.71, 4.60)	0.146	0.03 (-0.04, 0.09)	0.387
Heart disease
Yes	0.64 (0.04, 1.24)	0.036	11.83 (5.44, 18.22)	0.001	0.44 (0.21, 0.67)	0.0004
No	0.82 (0.56, 1.09)	<0.001	3.38 (0.41, 6.35)	0.026	0.28 (0.15, 0.40)	0.0001

Figure 1. The rate of overall renal replacement therapy (RRT) from the interrupted time series (ITS).

Discussion

Results showed that before implementing the universal dialysis policy, the trend of access to RRT slightly increased, while immediately after implementing the universal dialysis policy the trend sharply increased.

Before policy implementation in 2008, evidence showed that the rate of access to RRT slightly increased, possible as a result of the universal coverage policy not covering RRT. This meant that patients were responsible for medication payments (approximately 1,500 Thai baht per visit),¹⁹ which affected their decision to undergo the therapy. The long-term treatment of RRT and high payments made low-income patients unable to access a clinic and receive appropriate treatment.

Because of his, the National Health Security Office (NHSO) developed a kidney replacement therapy service system for patients with end-stage chronic renal failure in 2008. This policy was called ‘CAPD first’ and covered renal replacement therapy, kidney transplantation (KT), peritoneal dialysis (PD) and hemodialysis (HD). It provides peritoneal dialysis as the first choice and hemodialysis only for those who are unable to undergo peritoneal dialysis or who have a medical indication prohibiting PD. In the case of an old hemodialysis patient who does not voluntarily undergo peritoneal dialysis, the patient will have to pay 1/3 of the service fee (patients pay no more than 500 baht and NHSO pays 1,000 baht per time).¹⁹ This new system resulted in a significantly higher rate of RRT patients with stage 5 renal failure. There are still many patients with renal failure who have not decided to enter the treatment system. Our evidence confirmed that after implementing this policy in 2008, the rate of access to RRT sharply increased and it helped patients to receive treatment because they did not have to bear the expenses themselves but gained benefits allocated by the state.

Prevalence of renal replacement therapy in Thailand

The total yearly incidence of RRT increased by an average of 14.8% after the implementation of the universal coverage policy for renal replacement therapy (known first as CAPD) and the yearly incidence of all RRT modalities increased by an average of 34.8% in 2007 to 2009.²⁰ A report by Thai Renal Replacement Therapy (TRT) stated that the number of end-stage renal failure patients receiving renal replacement services increased from 68.34 per million in 2006 to over 181 per million in 2009 (or more than 11,500 new patients per year). The number of people receiving renal replacement services increased significantly from 419 per million to over 639 per million (or more than 40,000 in 2009).²¹ Our analysis results also showed that the linear trend of RRT after implementing the continuous policy slightly increased. Despite the expected increased volume of patients, the year-by-year growth rate of patients in all RRT modalities seemed to diminish over time.²² due to the CAPD first policy. HD is performed only for those who are unable to undergo peritoneal dialysis or who have a medical indication prohibiting PD. Therefore, HD dialysis will be covered.²³ Some patients who meet the criteria for HD decide not to enroll for CAPD due to lack of readiness for the treatment required, such as inappropriate accommodation, lack of caregivers, being unsure of self-cleaning processes at home and fear of infection after the procedure.²⁴

A slight increase in access to RRT services was in line with the assessment of access to services and the provision of renal replacement services under the health insurance system in Thailand. The expected number of patients who accessed the services was more than 35,000 in 2011, but there are only 19,000 cumulative cases under the UHC scheme. At the end of the fiscal year 2012, the number of patients was estimated at more than 48,000 patients, but the cumulative number of cases was only about 23,000.²⁵ In terms of service provision, some hospitals are unable to participate in the universal health insurance program or are unable to provide services. Due to the availability of human resources and location, a health care service might require patients to go to other hospitals that can provide services but are far from their homes, which made it inconvenient and costly for traveling. This might be a consideration when making the decision to select a treatment.

Strengths and limitations

Our study had some limitations. Firstly, data retrieved from other hospitals might have different methods that could have impacted data quality. Secondly, only two hospitals currently incorporate this scheme and one is a private hospital, so some data were incomplete. Finally, our results might not be generalizable to Thailand as a whole because we analyzed data from only one province. However, the strength of our study is an analysis using the interrupted time series model that accurately interpreted the results.

Conclusions

Our results revealed that after the universal coverage of renal replacement therapy policy implementation, the rate of treatment for RRT slightly increased. Extensive data collection from other health centers would be useful for further research. After the policy implementation, the trends in access to RRT slightly increased. This might be because patients were uncertain about using CAPD at their houses. To further increase the rate of RRT, policy makers should consider this point.

Data availability

Underlying data

figshare: Application of an interrupted time series analysis (ITS) to evaluate the effect of universal dialysis policy from 2006 to 2016 in a province of northeastern Thailand, https://doi.org/10.6084/m9.figshare.21456261.v5.²⁶

This project contains the dataset created during analysis.

Data are available under the terms of the Creative Commons Zero “No rights reserved” data waiver (CC0 1.0 Public domain dedication).

Acknowledgements

We would like to thank the Health Data Center officer in Roi Et Province for data preparation.

References

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Version 1

VERSION 1 PUBLISHED 21 Apr 2023

Author details Author details

¹ Epidemiology and Biostatistics Department, Faculty of Public Health, Khon Kaen University, Khon Kaen, 40000, Thailand
² ASEAN Cancer Epidemiology and Prevention Research Group, Khon Kaen University, Khon Kaen, 40000, Thailand
³ Division of Nephrology, Department of Internal Medicine Faculty, of Medicine, Khon Kaen University, Khon Kaen, 40000, Thailand

Piyalak Pukdeesamai
Roles: Conceptualization, Data Curation, Formal Analysis, Investigation, Methodology, Project Administration, Resources, Software, Validation, Visualization, Writing – Original Draft Preparation, Writing – Review & Editing

Pongdech Sarakarn
Roles: Conceptualization, Investigation, Methodology, Project Administration, Software, Supervision, Writing – Original Draft Preparation, Writing – Review & Editing

Sirirat Anutrakulchai
Roles: Investigation, Writing – Original Draft Preparation, Writing – Review & Editing

Competing interests

No competing interests were disclosed.

Grant information

The author(s) declared that no grants were involved in supporting this work.

Article Versions (1)

version 1

Published: 21 Apr 2023, 12:434

https://doi.org/10.12688/f1000research.128094.1

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© 2023 Pukdeesamai P et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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Pukdeesamai P, Sarakarn P and Anutrakulchai S. Application of an interrupted time series analysis (ITS) to evaluate the effect of universal dialysis policy from 2006 to 2016 in a province of northeastern Thailand [version 1; peer review: 1 approved with reservations]. F1000Research 2023, 12:434 (https://doi.org/10.12688/f1000research.128094.1)

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Open Peer Review

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Reviewer Report 04 Oct 2024

Yao Wu, Monash University, Clayton, Victoria, Australia

Approved with Reservations

https://doi.org/10.5256/f1000research.140653.r322049

This study assesses the impact of a universal dialysis policy from 2006 to 2016 in a province of north-eastern Thailand using an interrupted time series (ITS) analysis. The authors have made a good start, but the study requires further refinement, ... Continue reading

This study assesses the impact of a universal dialysis policy from 2006 to 2016 in a province of north-eastern Thailand using an interrupted time series (ITS) analysis. The authors have made a good start, but the study requires further refinement, particularly in terms of methodological justification and analysis. I provide the following suggestions for improvement:

The authors should provide more detailed background information on the universal dialysis policy in the Introduction or Methods sections. Specifically, they should explain the rationale behind this policy, particularly how it aims to increase access to renal replacement therapy (RRT).
The authors need to provide a clear rationale for choosing a 3-month interval for their analysis. Is this interval commonly used in similar studies, or does it reflect some meaningful aspect of the policy or clinical practices?
In the Methods section, the variable "X1" should represent time throughout the entire study period, rather than only the time before the intervention. The authors should refer to more recent studies to justify the use of their ITS analysis equation. I recommend citing literature such as the one linked here: [https://doi.org/10.1186/s12874-021-01306-w].(refer 1)
The authors mention that "only two hospitals currently incorporate this scheme." Given this, it would be more appropriate to focus on data from these two hospitals and use the other hospitals as a control group. This would enable the use of a controlled interrupted time series analysis, which would provide a more robust examination of the policy’s impact.
Figure 1: It is suggested that the authors include a counterfactual trend line in Figure 1 for the period after 2008. This would help in visualizing what the expected trend would have been without the policy intervention.
Authors stated that “Results showed that before implementing the universal dialysis policy, the trend of access to RRT slightly increased, while immediately after implementing the universal dialysis policy the trend sharply increased.” However, this interpretation is incorrect. The change in intercept reflects the immediate effects of the policy introduction, whereas the change in slope reflects the long-term effects. Based on Figure 1, it appears that the slope before 2008 (0.74) is higher than the slope after 2008 (0.29), indicating that the trend of access to RRT actually decreased after the policy was implemented. The results section should be revised to reflect this, and further discussion should be provided to justify and interpret this finding.

Is the work clearly and accurately presented and does it cite the current literature?

No
Is the study design appropriate and is the work technically sound?

Yes
Are sufficient details of methods and analysis provided to allow replication by others?

Yes
If applicable, is the statistical analysis and its interpretation appropriate?

Partly
Are all the source data underlying the results available to ensure full reproducibility?

Yes
Are the conclusions drawn adequately supported by the results?

Partly

References

1. Turner SL, Karahalios A, Forbes AB, Taljaard M, et al.: Comparison of six statistical methods for interrupted time series studies: empirical evaluation of 190 published series.BMC Med Res Methodol. 2021; 21 (1): 134 PubMed Abstract | Publisher Full Text

Competing Interests: No competing interests were disclosed.

Reviewer Expertise: Epidemiology

I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard, however I have significant reservations, as outlined above.

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Yao Wu, Monash University, Clayton, Australia

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Reviewer Report

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04 Oct 2024 | for Version 1

Yao Wu, Monash University, Clayton, Victoria, Australia

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Approved With Reservations

This study assesses the impact of a universal dialysis policy from 2006 to 2016 in a province of north-eastern Thailand using an interrupted time series (ITS) analysis. The authors have made a good start, but the study requires further refinement, particularly in terms of methodological justification and analysis. I provide the following suggestions for improvement:

The authors should provide more detailed background information on the universal dialysis policy in the Introduction or Methods sections. Specifically, they should explain the rationale behind this policy, particularly how it aims to increase access to renal replacement therapy (RRT).
The authors need to provide a clear rationale for choosing a 3-month interval for their analysis. Is this interval commonly used in similar studies, or does it reflect some meaningful aspect of the policy or clinical practices?
In the Methods section, the variable "X1" should represent time throughout the entire study period, rather than only the time before the intervention. The authors should refer to more recent studies to justify the use of their ITS analysis equation. I recommend citing literature such as the one linked here: [https://doi.org/10.1186/s12874-021-01306-w].(refer 1)
The authors mention that "only two hospitals currently incorporate this scheme." Given this, it would be more appropriate to focus on data from these two hospitals and use the other hospitals as a control group. This would enable the use of a controlled interrupted time series analysis, which would provide a more robust examination of the policy’s impact.
Figure 1: It is suggested that the authors include a counterfactual trend line in Figure 1 for the period after 2008. This would help in visualizing what the expected trend would have been without the policy intervention.
Authors stated that “Results showed that before implementing the universal dialysis policy, the trend of access to RRT slightly increased, while immediately after implementing the universal dialysis policy the trend sharply increased.” However, this interpretation is incorrect. The change in intercept reflects the immediate effects of the policy introduction, whereas the change in slope reflects the long-term effects. Based on Figure 1, it appears that the slope before 2008 (0.74) is higher than the slope after 2008 (0.29), indicating that the trend of access to RRT actually decreased after the policy was implemented. The results section should be revised to reflect this, and further discussion should be provided to justify and interpret this finding.

Is the work clearly and accurately presented and does it cite the current literature?

No
Is the study design appropriate and is the work technically sound?

Yes
Are sufficient details of methods and analysis provided to allow replication by others?

Yes
If applicable, is the statistical analysis and its interpretation appropriate?

Partly
Are all the source data underlying the results available to ensure full reproducibility?

Yes
Are the conclusions drawn adequately supported by the results?

Partly

References

1. Turner SL, Karahalios A, Forbes AB, Taljaard M, et al.: Comparison of six statistical methods for interrupted time series studies: empirical evaluation of 190 published series.BMC Med Res Methodol. 2021; 21 (1): 134 PubMed Abstract | Publisher Full Text

Competing Interests

No competing interests were disclosed.

Reviewer Expertise

Epidemiology

I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard, however I have significant reservations, as outlined above.

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Application of an interrupted time series analysis (ITS) to evaluate the effect of universal dialysis policy from 2006 to 2016 in a province of northeastern Thailand

Abstract

Keywords

Introduction

Methods

Data sources

Ethical considerations and consent

Study variables

Data analysis

Results

Table 1. Description of chronic kidney disease (CKD) stage-5 patients, before and after policy implementation.

Table 2. The rate of patients who have had renal replacement therapy (RRT) by sex, age, and primary cause.

Table 3. Results from the interrupted time series (ITS) on the rate of access to renal replacement therapy before and after policy implementation.

Figure 1. The rate of overall renal replacement therapy (RRT) from the interrupted time series (ITS).

Discussion

Prevalence of renal replacement therapy in Thailand

Strengths and limitations

Conclusions

Data availability

Underlying data

Acknowledgements

References

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