Authors’ Response to Peer Reviews of “The Association of Shared Care Networks With 30-Day Heart Failure Excessive Hospital Readmissions: Longitudinal Observational Study”

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Authors' Comment
We appreciate the recognition that HF excessive hospital readmissions is a very current topic.

Specific Comments
Major Comments: Comment 1 1.In "study population and design" in "methods," the authors mentioned, "hospitals with less than 2 repeated measures of higher-than-expected HF readmission in the HRRP (Hospital Reduction Readmission Program) or without discharge data in the OSHPD (Office of Statewide Health Planning and Development) were excluded."Does this mean this study only considered hospitals with repeated higher-than-expected HF readmission?
Ignoring hospitals without repeated higher-than-expected HF readmission may introduce bias to the analysis.Please clarify why you have chosen this data inclusion criterion.

Authors' Comment
We appreciate the comment about the exclusion of hospitals with less than 2 repeated measures and the bias such exclusion may produce.Given that the study design is longitudinal using generalized estimating equations (GEEs), repeated measures are required.Nevertheless, we rewrote this whole section, which is now as follows: "Study Design, Study Setting, and Participants This is an observational longitudinal study.The study setting was hospitals in California, US during the period from 2012 to 2017.Participants were all hospitals reported in the Hospital Readmissions Reduction Program (HRRP) (6).The eligibility criteria were as follows: At least 2 repeated measures of higher-than-expected HF readmission in the HRRP and availability of discharge data from the Office of Statewide Health Planning and Development (OSHPD) (16).These criteria enabled, respectively, carrying out a longitudinal study which requires repeated measures and linking data from the HRRP with date from OSHPD.Between 233 and 237 hospitals in California were included depending on the year.Ethical approval was unnecessary because all data was at the hospital-level and was already made publicly available from both HRRP and OSHPD.All code, processed data, built networks, and data analysis resulting from this work are available on the Open Science Framework (OSF) repository of this work (37)." Major Comments: Comment 2 2. In "data sources" in "methods," the authors collected excessive readmission ratio (ERR) data from 2012 to 2017.In almost every year, the HRRP updated the inclusion criteria of HF readmission (eg, lists of eligible diagnosis codes and procedure codes in the planned readmission algorithm).In this case, how did you fairly compare the ERR across different years?

Authors' Comment
This is a very insightful comment and indeed requires extra discussion.The ERR is a risk-standardized 30-day readmission ratio.It is used by the HRRP to assess excess hospital readmissions and calculate hospital penalties [3].The ERR has been used in longitudinal studies including the years of this study before [3][4][5].
The ERR is calculated by dividing the "predicted readmissions" (p) to "expected readmissions" (e).Using a hierarchical generalized linear model (HGLM), both "predicted" (p) and "expected" (e) readmissions are estimated using an "adjusted average intercept over all hospitals" (u), but the number of "predicted readmissions" (p), in addition, is estimated using a hospital-specific intercept deviation (a = u + w) from the "adjusted average intercept over all hospitals" (u).Such methodology, well documented in the Condition-Specific Readmission Measures Updates and Specifications Report from the Centers for Medicare & Medicaid Services (CMS) [6], makes the ERR an appropriate instrument for comparing hospitals within and between years.
The following text was included in "data sources" in "methods": "The ERR is calculated dividing the predicted readmissions to expected readmissions.Using a hierarchical generalized linear model (HGLM), both predicted and expected readmissions are estimated using an adjusted average intercept over all hospitals, but predicted readmissions, in addition, is estimated using a hospital-specific intercept deviation from the adjusted average intercept over all hospitals.Such methodology, well documented in the Condition-Specific Readmission Measures Updates and Specifications Report from the Centers for Medicare & Medicaid Services (CMS) [7], makes the ERR an appropriate instrument for comparing hospitals within and between years." Major Comments: Comment 3 3.Is the "Uncovering Shared Care Areas and Localization Index from Hospital-Patient Discharge Data" in "methods" a literature review of other studies or the method the authors used in this study?Please clarify.If it is a literature review, it should go in the "introduction."

Authors' Comment
Thank you for mentioning the methods in this subsection.Though it may appear to be a literature review, we are only specifying the parameters that were considered for each algorithm.

Reviewer BX [7]
Major Comments: Comment 1 • Title: For this study, please include the type of study in the title.If you are considering 30-day readmission, please specify it in the title.

Authors' Comment
We appreciate this comment, and following your suggestion, we changed the title to "Association of Shared Care Networks with 30-Day Heart Failure Excessive Hospital Readmissions: Longitudinal Observational Study."We hope this new title is now appropriate.

Major Comments: Comment 2
• Abstract: Please move the objective section to the end of the background section, and it is recommended that it is written the same as in the study title.

Authors' Comment
Thank you very much.Following your suggestion, we changed the objective to "This study aimed to evaluate the association of shared care networks with 30-day heart failure excessive readmission rates using a longitudinal observational study" to be written the same as the study title.We would love to move it to the end of the background section, but it seems that the Objective section is mandatory.

Major Comments: Comment 3
• Methods: Please start this section with the study design.Study setting, study variables, and outcomes and their measurements should be mentioned, briefly.Eligibility criteria have not been provided.

Authors' Comment
Thank you for your suggestion.We rewrote the Methods section.Its first section is now "Study Design, Study Setting, and Participants."

Major Comments: Comment 4
• Methods: ERR: I think it is excessive readmission risk ratio because no person-year has been reported.Thus, to improve the reporting, please revise it in the whole document.

Authors' Comment
Thanks for the suggestion.We would rather use the same name used in the literature [6].

Major Comments: Comment 5
• Results: To facilitate the interpretation of the study results, please convert beta coefficients by exponentiating them.

Authors' Comment
We understand the need of converting beta coefficients when dependent variables are dichotomous (binary).In our case, the ERR is not dichotomous but a continuous variable that can be less than or greater than 1 such as 0.92 or 1.23 depending on the presence or absence of excessive hospital readmissions.Therefore, we used a GEE with a Gaussian family without a Logit link function.In this case, we understand that converting the beta coefficients would not be appropriate because in their current form they express, on average, a 1-unit of change in the predictor variable.
We modified the text to clarify potential misunderstandings.
We included the following text in "data sources" in "methods": "The ERR is calculated dividing the predicted readmissions to expected readmissions.Using a hierarchical generalized linear model (HGLM), both predicted and expected readmissions are estimated using an adjusted average intercept over all hospitals, but predicted readmissions, in addition, is estimated using a hospital-specific intercept deviation from the adjusted average intercept over all hospitals.Such methodology, well documented in the Condition-Specific Readmission Measures Updates and Specifications Report from the Centers for Medicare & Medicaid Services (CMS) [6], makes the ERR an appropriate instrument for comparing hospitals within and between years."

Major Comments: Comment 6
• Please use expanded forms of the abbreviations the first time they are mentioned.The expanded form of some abbreviations has not been provided.

Authors' Comment
We appreciate this comment from the reviewer.The paper was revised to use the expanded form of the abbreviations for the first time.Additionally, we included all abbreviations in the Abbreviations section in alphabetic order.

Authors' Comment
We appreciate the encouragement to write keywords according to the MeSH system.We changed all our keywords as follows: "Patient Readmission; Quality Assurance, Health Care; Catchment Area, Health; Community Networks; Regional Medical Programs."

Major Comments: Comment 8
• Methods: It is recommended to write this section according to the STROBE (Strengthening the Reporting of Observational Studies in Epidemiology) standard writing and refer to it in the first paragraph of the Methods section.

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for Medicare & Medicaid Services ED: emergency department ERR: excessive readmission ratios HF: heart failure HGLM: hierarchical generalized linear model HRRP: Hospital Reduction Readmission Program GEE: generalized estimating equations LI: localization index LVAD: Left Ventricular Assisted Devices OLS: ordinary least squares OSHPD: Office of Statewide Health Planning and Development SCA: shared care area STROBE: STrengthening the Reporting of OBservational studies in Epidemiology OSF: Open Science Framework UDS: Uniform Data System ZCTA: ZIP Code Tabulation Area"Major Comments: CommentKeywords: Please write these according to the Medical Subject Headings (MeSH) system.•Introduction: The necessity of this study is not clear.Please provide a paragraph about the importance and necessity of this study and why you designed and conducted this study.