Identifying opioid agonist treatment prescriber networks from health administrative data: A validation study

Megan Kurz; Mark Tatangelo; Kristen A. Morin; Michelle Zanette; Emanuel Krebs; David C. Marsh; Bohdan Nosyk

doi:10.1371/journal.pone.0322064

Peer Review History

Original SubmissionOctober 22, 2024
22 Oct 2024 Author Response https://doi.org/10.1371/journal.pone.0322064.r001
15 Jan 2025 Decision Letter - Marianna Mazza, Editor PONE-D-24-47217Identifying opioid agonist treatment prescriber networks from health administrative data: a validation study.PLOS ONE Dear Dr. Nosyk, Thank you for submitting your manuscript to PLOS ONE. After careful consideration, we feel that it has merit but does not fully meet PLOS ONE’s publication criteria as it currently stands. Therefore, we invite you to submit a revised version of the manuscript that addresses the points raised by reviewers. In particular, there are concerns expressed by reviewers regarding methodology. Please submit your revised manuscript by Mar 01 2025 11:59PM. If you will need more time than this to complete your revisions, please reply to this message or contact the journal office at plosone@plos.org . When you're ready to submit your revision, log on to https://www.editorialmanager.com/pone/ and select the 'Submissions Needing Revision' folder to locate your manuscript file. 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[Note: HTML markup is below. Please do not edit.] Reviewers' comments: Reviewer's Responses to Questions Comments to the Author 1. Is the manuscript technically sound, and do the data support the conclusions? The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented. Reviewer #1: Yes Reviewer #2: Yes ******** 2. Has the statistical analysis been performed appropriately and rigorously? Reviewer #1: Yes Reviewer #2: Yes ****** 3. Have the authors made all data underlying the findings in their manuscript fully available? The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified. Reviewer #1: No Reviewer #2: Yes ****** 4. Is the manuscript presented in an intelligible fashion and written in standard English? PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here. Reviewer #1: Yes Reviewer #2: Yes ****** 5. Review Comments to the Author Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters) Reviewer #1: The present manuscript leverages the health administrative data to construct networks among the prescribers and detect communities among them. The authors further validate the results with the ground truth of shared facilities and show that the network analyses can be used to identify the patterns in prescribing behaviors. I think the paper is pretty solid and easy to follow. One suggestion I have is that I think it would be beneficial if the authors could provide more technical details. For instance, the authors mention that they have performed various robustness checks using different methods to construct 250 different networks. Please describe the characteristics of these networks, such as their number of nodes and number of edges. Also, the time range of the data is not very clear to me. In the results section, it appears that health administrative data from seven years was used to create different versions of the networks. However, the ground truth data was only from one year. I think the authors need to justify these choices and also test the impact of using data from different time ranges to create networks. Reviewer #2: I appreciate the authors' time and effort in studying OAT prescriber networks. This type of analysis is rarely conducted, and I commend the authors for addressing such an under-explored area. However, a key concern is the practical implications of the findings. What can we do with the results of this analysis? While the paper includes some discussion on this, I found the arguments to be somewhat underdeveloped and not entirely convincing. Another important point is the justification for the chosen statistical methods. For instance, while modularity maximization is a widely used approach for identifying subgroups, it would be helpful to explain why this method was preferred over other, potentially more advanced alternatives. Additionally, the definitions of certain evaluation measures could be clarified. For example, readers without a strong statistical background may not be familiar with terms such as Positive Predictive Value (PPV) or Negative Predictive Value (NPV). Providing clearer definitions or brief explanations would improve accessibility and understanding for a broader audience. ****** 6. PLOS authors have the option to publish the peer review history of their article (what does this mean? ). If published, this will include your full peer review and any attached files. 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Revision 1
11 Mar 2025 Author Response Reviewers' comments: Reviewer's Responses to Questions Comments to the Author Reviewer #1: The present manuscript leverages the health administrative data to construct networks among the prescribers and detect communities among them. The authors further validate the results with the ground truth of shared facilities and show that the network analyses can be used to identify the patterns in prescribing behaviors. I think the paper is pretty solid and easy to follow. One suggestion I have is that I think it would be beneficial if the authors could provide more technical details. For instance, the authors mention that they have performed various robustness checks using different methods to construct 250 different networks. Please describe the characteristics of these networks, such as their number of nodes and number of edges. Response: To describe the characteristics across each different network definition, we have added an additional table (S1 Table) as supplementary material. The table summarizes characteristics of the networks for each calendar year. Also, the time range of the data is not very clear to me. In the results section, it appears that health administrative data from seven years was used to create different versions of the networks. However, the ground truth data was only from one year. I think the authors need to justify these choices and also test the impact of using data from different time ranges to create networks. Response: As each network was created on an annual basis, and we have added an additional table (S2 Table) as supplementary material that describes the characteristics compared to the ground truth for each calendar year. Reviewer #2: I appreciate the authors' time and effort in studying OAT prescriber networks. This type of analysis is rarely conducted, and I commend the authors for addressing such an under-explored area. However, a key concern is the practical implications of the findings. What can we do with the results of this analysis? While the paper includes some discussion on this, I found the arguments to be somewhat underdeveloped and not entirely convincing. Response: We have edited the discussion to highlight more on how this validation can be used with administrative data for opioid use disorder in particular. (page 14, paragraph 2, line 255): “Generally, use of administrative databases for evaluation and quality improvement has limitations, particularly with missing information and the lack of identifying indicators for clinical sites and referral networks. These limitations are exceptionally relevant for clinical OUD management in the fentanyl-era, when rapidly accessible specialty addictions clinics(40) and collaborative care(1, 4) have been introduced as one component to combat the declines in treatment retention and persistently high rates of overdose death among people who use drugs(8, 41-43). By validating the identification of clinics, we provide an opportunity to distinguish and evaluate clinical caseloads and clinic-level performance among databases that lack clinic-level identifiers, thus improving the use of administrative health records for ongoing evaluation and quality improvement particularly for opioid use disorder. Additionally in other clinical areas, …” (page 15, paragraph 2, line 282): “Identifying facilities with administrative data otherwise provides an opportunity for enhanced control of confounding, which may be otherwise unmeasured in comparative effectiveness studies, when there is a lack of clinic-level identifiers. …” Another important point is the justification for the chosen statistical methods. For instance, while modularity maximization is a widely used approach for identifying subgroups, it would be helpful to explain why this method was preferred over other, potentially more advanced alternatives. Response: We previously tested two different community detection alternatives, including: Walktrap and Louvain and have made this clearer. While Louvain also applies modularity maximization, Walktrap does not. However, we added the Label Propagation algorithm to test an additional community detection alternative that does not optimize modularity. (page 9, paragraph 1, line 167): “In addition to the modularity maximization method to identify disjoint communities, we compared the Walktrap(38) and Louvain(39) methods to test different identification algorithms … We also compared the Label Propagation(40) algorithm, although it has not been used in community detection studies among prescribers.” (page 11, paragraph 2, line 213): “Results from the Walktrap, Louvain, and Label Propagation algorithms had lower specificity and PPV compared to the modularity maximization algorithm … Label Propagation had the highest sensitivity, with the drawbacks of the lowest PPV and specificity.” Additionally, the definitions of certain evaluation measures could be clarified. For example, readers without a strong statistical background may not be familiar with terms such as Positive Predictive Value (PPV) or Negative Predictive Value (NPV). Providing clearer definitions or brief explanations would improve accessibility and understanding for a broader audience. Response: We have added a basic descriptive of each term, relative to their meaning for this analysis. (page 8, paragraph 2, line 144): “We also calculated the sensitivity (probability for a physician pair to be grouped in a network-identified community together when they are attached to the same clinic), specificity (probability for a physician pair to be in separate network-identified communities when they do not practice at the same clinics), positive predictive value (PPV; probability of a physician pair practicing at the same clinic when they are grouped in a network-identified community) and negative predictive value (NPV; probability of a physician pair practicing at different clinics when they are in different network-identified communities).” Attachments Attachment Submitted filename: PrescriberNetworkValidation_reviewresponse_v1.docx https://doi.org/10.1371/journal.pone.0322064.r003
16 Mar 2025 Decision Letter - Marianna Mazza, Editor Identifying opioid agonist treatment prescriber networks from health administrative data: a validation study. PONE-D-24-47217R1 Dear Dr. Nosyk, We’re pleased to inform you that your manuscript has been judged scientifically suitable for publication and will be formally accepted for publication once it meets all outstanding technical requirements. Within one week, you’ll receive an e-mail detailing the required amendments. When these have been addressed, you’ll receive a formal acceptance letter and your manuscript will be scheduled for publication. An invoice will be generated when your article is formally accepted. Please note, if your institution has a publishing partnership with PLOS and your article meets the relevant criteria, all or part of your publication costs will be covered. Please make sure your user information is up-to-date by logging into Editorial Manager at Editorial Manager® and clicking the ‘Update My Information' link at the top of the page. If you have any questions relating to publication charges, please contact our Author Billing department directly at authorbilling@plos.org. If your institution or institutions have a press office, please notify them about your upcoming paper to help maximize its impact. If they’ll be preparing press materials, please inform our press team as soon as possible -- no later than 48 hours after receiving the formal acceptance. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information, please contact onepress@plos.org. Kind regards, Marianna Mazza Academic Editor PLOS ONE Additional Editor Comments (optional): Reviewers' comments: https://doi.org/10.1371/journal.pone.0322064.r004
Formally Accepted
Acceptance Letter - Marianna Mazza, Editor PONE-D-24-47217R1 PLOS ONE Dear Dr. Nosyk, I'm pleased to inform you that your manuscript has been deemed suitable for publication in PLOS ONE. Congratulations! Your manuscript is now being handed over to our production team. At this stage, our production department will prepare your paper for publication. This includes ensuring the following: * All references, tables, and figures are properly cited * All relevant supporting information is included in the manuscript submission, * There are no issues that prevent the paper from being properly typeset You will receive further instructions from the production team, including instructions on how to review your proof when it is ready. Please keep in mind that we are working through a large volume of accepted articles, so please give us a few days to review your paper and let you know the next and final steps. Lastly, if your institution or institutions have a press office, please let them know about your upcoming paper now to help maximize its impact. If they'll be preparing press materials, please inform our press team within the next 48 hours. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information, please contact onepress@plos.org. If we can help with anything else, please email us at customercare@plos.org. Thank you for submitting your work to PLOS ONE and supporting open access. Kind regards, PLOS ONE Editorial Office Staff on behalf of Dr. Marianna Mazza Academic Editor PLOS ONE https://doi.org/10.1371/journal.pone.0322064.r005

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