Time series modeling to estimate unrecorded burden of 12 symptomatic medical conditions among United States Medicare beneficiaries during the COVID-19 pandemic
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Abstract
Objective
U.S. healthcare utilization declined during the COVID-19 pandemic, potentially leading to spurious drops in disease incidence recorded in administrative healthcare datasets used for public health surveillance. We used time series modeling to characterize the magnitude and duration of the COVID-19 pandemic’s impact on claims-based monthly incidence of 12 symptomatic conditions among Medicare beneficiaries aged ≥65 years.
Methods
Time series of observed monthly incidence of each condition were generated using Medicare claims data from January 2016–May 2021. Incidence time series were decomposed through seasonal and trend decomposition using Loess, resulting in seasonal, trend, and remainder components. We fit a non-linear mixed effects model to remainder time series components and used it to estimate underlying incidence and number of unrecorded cases of each condition during the pandemic period.
Results
Observed incidence of all 12 conditions declined steeply in March 2020 with nadirs in April 2020, generally followed by return to pre-pandemic trends. The relative magnitude of the decrease varied by condition, but month of onset and duration did not. Estimated unrecorded cases during March 2020–May 2021 ranged from 9,543 (95% confidence interval [CI]: 854–15,703) for herpes zoster to 236,244 (95% CI: 188,583–292,369) for cataracts.
Conclusions
Due to reduced healthcare utilization during the COVID-19 pandemic, claims-based data underestimate incidence of non-COVID-19 conditions. Time series modeling can be used to quantify this underestimation, facilitating longitudinal analyses of disease incidence pre- and post-pandemic.
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SciScore for 10.1101/2022.05.09.22274870: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
NIH rigor criteria are not applicable to paper type.Table 2: Resources
Software and Algorithms Sentences Resources Data preprocessing was performed using SAS 9.4 (SAS Institute, Cary, NC). SAS Institutesuggested: (Statistical Analysis System, RRID:SCR_008567)Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).
Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:This study has at least three limitations. First, we used diagnostic coding to identify incident cases and were unable to validate cases with medical record review. Second, …
SciScore for 10.1101/2022.05.09.22274870: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
NIH rigor criteria are not applicable to paper type.Table 2: Resources
Software and Algorithms Sentences Resources Data preprocessing was performed using SAS 9.4 (SAS Institute, Cary, NC). SAS Institutesuggested: (Statistical Analysis System, RRID:SCR_008567)Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).
Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:This study has at least three limitations. First, we used diagnostic coding to identify incident cases and were unable to validate cases with medical record review. Second, incidence of the 12 conditions in persons aged ≥65 years may not be representative of incidence other acute conditions with differing severity or in other age groups, though our findings support those of other studies.1, 3, 6, 10 Finally, a control group unaffected by COVID-19 was not available, so we could not rigorously attribute observed decreases in observed incidence to pandemic-associated effects. However, there was no analogous March–May drop in incidence during four years of pre-pandemic data for the same conditions in the same population, bolstering confidence that the changes were pandemic-associated. Administrative healthcare data will continue to play a critical role in public health surveillance and other longitudinal epidemiologic studies. However, because the COVID-19 pandemic has challenged interpretation of observed disease incidence trends, analytic methods must address otherwise anomalous changes in incidence. We propose a time series modeling approach to account for the effects of healthcare avoidance and reduced access on longitudinal disease incidence data that may facilitate continued surveillance using administrative data in the COVID-19 era.
Results from TrialIdentifier: No clinical trial numbers were referenced.
Results from Barzooka: We did not find any issues relating to the usage of bar graphs.
Results from JetFighter: We did not find any issues relating to colormaps.
Results from rtransparent:- Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
- Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
- No protocol registration statement was detected.
Results from scite Reference Check: We found no unreliable references.
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