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      Comanagement with rheumatology and prescription biologics filled during pregnancy in women with rheumatic diseases: a retrospective analysis of US administrative claims data

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          Abstract

          Objectives

          To evaluate comanagement with rheumatology and biological prescriptions filled during pregnancy among women with axial spondyloarthritis (axSpA), rheumatoid arthritis (RA) or psoriatic arthritis (PsA) and to examine factors associated with receiving comanagement with rheumatology during pregnancy.

          Design

          A retrospective analysis of US claims data.

          Setting

          Commercially insured enrollees using data from the 2013–2018 IBM MarketScan Commercial Claims and Encounters Database.

          Participants

          We identified 4131 pregnant women aged ≤55 years from the 2013–2018 IBM MarketScan Commercial Claims and Encounters Database with an International Classification of Disease, 9th Revision/10th Revision codes for RA, axSpA or PsA, with continuous enrolment at ≥3 months before the date of the last menstrual period (LMP) (index date) and throughout pregnancy.

          Primary outcomes

          Filled biologics (prescriptions and infusions) claims were categorised by 90 days before the LMP and trimester, as were primary care, obstetrician and rheumatological claims.

          Results

          The prevalence of axSpA, RA and PsA was 0.7%, 0.2% and 0.04% among reproductive age women. The average maternal age was 32.7 years (SD 5.7). During pregnancy, 9.1% of those with axSpA (n=2,410) and 56.4% of those with RA/PsA (n=1,721) had a rheumatological claim. Biologics claims were less common among those with axSpA (90 days before LMP: 1.6%, during pregnancy: 1.1%) than those with RA/PsA (90 days before LMP: 11.9%, during pregnancy: 6.9%). Medications during pregnancy included corticosteroids (axSpA: 0.3%, RA/PsA: 2.2%), non-biological disease-modifying antirheumatic drugs (axSpA: 0.2%, RA/PsA: 1.7%), non-steroidal anti-inflammatory drugs (axSpA: 0.2%, RA/PsA: 1.3%) and opioids (axSpA: 0.2%, RA/PsA: 0.6%). Established rheumatological care and biologics claims during the 90 days before LMP showed good prediction accuracy for receiving comanagement with rheumatology during pregnancy (axSpA: area under the receiver operator curve (AUC) 0.73, RA/PsA: AUC 0.70).

          Conclusion

          Comanagement with rheumatology during pregnancy occurs infrequently, especially for women with axSpA. Biologics claims during pregnancy may not align with published guidelines. Future research is warranted to improve comanagement with rheumatology during pregnancy.

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          Most cited references60

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          A new method of classifying prognostic comorbidity in longitudinal studies: Development and validation

          The objective of this study was to develop a prospectively applicable method for classifying comorbid conditions which might alter the risk of mortality for use in longitudinal studies. A weighted index that takes into account the number and the seriousness of comorbid disease was developed in a cohort of 559 medical patients. The 1-yr mortality rates for the different scores were: "0", 12% (181); "1-2", 26% (225); "3-4", 52% (71); and "greater than or equal to 5", 85% (82). The index was tested for its ability to predict risk of death from comorbid disease in the second cohort of 685 patients during a 10-yr follow-up. The percent of patients who died of comorbid disease for the different scores were: "0", 8% (588); "1", 25% (54); "2", 48% (25); "greater than or equal to 3", 59% (18). With each increased level of the comorbidity index, there were stepwise increases in the cumulative mortality attributable to comorbid disease (log rank chi 2 = 165; p less than 0.0001). In this longer follow-up, age was also a predictor of mortality (p less than 0.001). The new index performed similarly to a previous system devised by Kaplan and Feinstein. The method of classifying comorbidity provides a simple, readily applicable and valid method of estimating risk of death from comorbid disease for use in longitudinal studies. Further work in larger populations is still required to refine the approach because the number of patients with any given condition in this study was relatively small.
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            Applied Logistic Regression

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              Adapting a clinical comorbidity index for use with ICD-9-CM administrative databases

              R Deyo (1992)
              Administrative databases are increasingly used for studying outcomes of medical care. Valid inferences from such data require the ability to account for disease severity and comorbid conditions. We adapted a clinical comorbidity index, designed for use with medical records, for research relying on International Classification of Diseases (ICD-9-CM) diagnosis and procedure codes. The association of this adapted index with health outcomes and resource use was then examined with a sample of Medicare beneficiaries who underwent lumbar spine surgery in 1985 (n = 27,111). The index was associated in the expected direction with postoperative complications, mortality, blood transfusion, discharge to nursing home, length of hospital stay, and hospital charges. These associations were observed whether the index incorporated data from multiple hospitalizations over a year's time, or just from the index surgical admission. They also persisted after controlling for patient age. We conclude that the adapted comorbidity index will be useful in studies of disease outcome and resource use employing administrative databases.
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                Author and article information

                Journal
                BMJ Open
                BMJ Open
                bmjopen
                bmjopen
                BMJ Open
                BMJ Publishing Group (BMA House, Tavistock Square, London, WC1H 9JR )
                2044-6055
                2022
                22 December 2022
                : 12
                : 12
                : e065189
                Affiliations
                [1 ]departmentDivision of Epidemiology, Department of Population and Quantitative Health Sciences , UMass Chan Medical School , Worcester, Massachusetts, USA
                [2 ]departmentClinical and Population Health Research Program, Graduate School of Biomedical Sciences , UMass Chan Medical School , Worcester, Massachusetts, USA
                [3 ]departmentDivision of Rheumatology , UMass Memorial Medical Center , Worcester, Massachusetts, USA
                [4 ]departmentDivision of Rheumatology, Department of Medicine , UMass Chan Medical School , Worcester, Massachusetts, USA
                Author notes
                [Correspondence to ] Dr Shao-Hsien Liu; shaohsien.liu@ 123456umassmed.edu
                Author information
                http://orcid.org/0000-0002-9199-798X
                http://orcid.org/0000-0003-4317-4986
                Article
                bmjopen-2022-065189
                10.1136/bmjopen-2022-065189
                9791456
                36549721
                60af7321-d3cc-4ca3-b83e-1584b0348360
                © Author(s) (or their employer(s)) 2022. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ.

                This is an open access article distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited, appropriate credit is given, any changes made indicated, and the use is non-commercial. See:  http://creativecommons.org/licenses/by-nc/4.0/.

                History
                : 27 May 2022
                : 02 December 2022
                Funding
                Funded by: Charitable contribution to the UMass Memorial Foundation from Timothy S. and Elaine L. Peterson;
                Award ID: N/A
                Categories
                Epidemiology
                1506
                1692
                Original research
                Custom metadata
                unlocked

                Medicine
                epidemiology,rheumatology,health services administration & management
                Medicine
                epidemiology, rheumatology, health services administration & management

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