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      Lower Risk for COVID-19 Hospitalization among Patients in the United States with Past Vaccinations for Herpes Zoster and Tetanus, Diphtheria and Pertussis

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          Abstract

          Influenza, tetanus, diphtheria, and herpes zoster (HZ) vaccination received within 10 years of the COVID-19 pandemic have been associated with less severe COVID-19 infection. We expanded on this evidence to determine if a receiving two different vaccinations (i.e., HZ and tetanus, diphtheria, and pertussis (Tdap)) was associated with a lower risk for COVID-19 hospitalization. De-identified medical record data from a large mid-western health care system was used to determine if, compared to those with neither HZ or Tdap vaccination, patients with either HZ or Tdap and patients with both HZ and Tdap vaccination had lower risk for COVID-19 hospitalization between 4/1/2020 and 12/31/2020. Confounding was controlled using entropy balancing. Patients (n=363,293) were 71.5 (±8.4) years of age, 57.8% female and 89.2% White race. Prior to controlling for confounding, as compared to patients without either vaccination, those that had either HZ or Tdap were significantly less likely to have a COVID-19 hospitalization (RR=0.85; 95%CI: 0.75-0.95). The risk for hospitalization decreased further among those with both HZ and Tdap vaccination (RR=0.45; 95%CI:0.28-0.71). After controlling for confounding, including healthy patient bias, receiving both vs. neither vaccinations remained significantly associated with a lower risk of COVID-19 hospitalization (RR= 0.48; 95%CI: 0.26-0.90). Receiving both Tdap and HZ vaccination is associated with lower risk for COVID-19 hospitalization. Whether there is any benefit of past vaccination exposure in COVID-19 vaccinated patients should be investigated.

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          Coding algorithms for defining comorbidities in ICD-9-CM and ICD-10 administrative data.

          Implementation of the International Statistical Classification of Disease and Related Health Problems, 10th Revision (ICD-10) coding system presents challenges for using administrative data. Recognizing this, we conducted a multistep process to develop ICD-10 coding algorithms to define Charlson and Elixhauser comorbidities in administrative data and assess the performance of the resulting algorithms. ICD-10 coding algorithms were developed by "translation" of the ICD-9-CM codes constituting Deyo's (for Charlson comorbidities) and Elixhauser's coding algorithms and by physicians' assessment of the face-validity of selected ICD-10 codes. The process of carefully developing ICD-10 algorithms also produced modified and enhanced ICD-9-CM coding algorithms for the Charlson and Elixhauser comorbidities. We then used data on in-patients aged 18 years and older in ICD-9-CM and ICD-10 administrative hospital discharge data from a Canadian health region to assess the comorbidity frequencies and mortality prediction achieved by the original ICD-9-CM algorithms, the enhanced ICD-9-CM algorithms, and the new ICD-10 coding algorithms. Among 56,585 patients in the ICD-9-CM data and 58,805 patients in the ICD-10 data, frequencies of the 17 Charlson comorbidities and the 30 Elixhauser comorbidities remained generally similar across algorithms. The new ICD-10 and enhanced ICD-9-CM coding algorithms either matched or outperformed the original Deyo and Elixhauser ICD-9-CM coding algorithms in predicting in-hospital mortality. The C-statistic was 0.842 for Deyo's ICD-9-CM coding algorithm, 0.860 for the ICD-10 coding algorithm, and 0.859 for the enhanced ICD-9-CM coding algorithm, 0.868 for the original Elixhauser ICD-9-CM coding algorithm, 0.870 for the ICD-10 coding algorithm and 0.878 for the enhanced ICD-9-CM coding algorithm. These newly developed ICD-10 and ICD-9-CM comorbidity coding algorithms produce similar estimates of comorbidity prevalence in administrative data, and may outperform existing ICD-9-CM coding algorithms.
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            Comorbidity measures for use with administrative data.

            This study attempts to develop a comprehensive set of comorbidity measures for use with large administrative inpatient datasets. The study involved clinical and empirical review of comorbidity measures, development of a framework that attempts to segregate comorbidities from other aspects of the patient's condition, development of a comorbidity algorithm, and testing on heterogeneous and homogeneous patient groups. Data were drawn from all adult, nonmaternal inpatients from 438 acute care hospitals in California in 1992 (n = 1,779,167). Outcome measures were those commonly available in administrative data: length of stay, hospital charges, and in-hospital death. A comprehensive set of 30 comorbidity measures was developed. The comorbidities were associated with substantial increases in length of stay, hospital charges, and mortality both for heterogeneous and homogeneous disease groups. Several comorbidities are described that are important predictors of outcomes, yet commonly are not measured. These include mental disorders, drug and alcohol abuse, obesity, coagulopathy, weight loss, and fluid and electrolyte disorders. The comorbidities had independent effects on outcomes and probably should not be simplified as an index because they affect outcomes differently among different patient groups. The present method addresses some of the limitations of previous measures. It is based on a comprehensive approach to identifying comorbidities and separates them from the primary reason for hospitalization, resulting in an expanded set of comorbidities that easily is applied without further refinement to administrative data for a wide range of diseases.
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              Is Open Access

              Moving towards best practice when using inverse probability of treatment weighting (IPTW) using the propensity score to estimate causal treatment effects in observational studies

              The propensity score is defined as a subject's probability of treatment selection, conditional on observed baseline covariates. Weighting subjects by the inverse probability of treatment received creates a synthetic sample in which treatment assignment is independent of measured baseline covariates. Inverse probability of treatment weighting (IPTW) using the propensity score allows one to obtain unbiased estimates of average treatment effects. However, these estimates are only valid if there are no residual systematic differences in observed baseline characteristics between treated and control subjects in the sample weighted by the estimated inverse probability of treatment. We report on a systematic literature review, in which we found that the use of IPTW has increased rapidly in recent years, but that in the most recent year, a majority of studies did not formally examine whether weighting balanced measured covariates between treatment groups. We then proceed to describe a suite of quantitative and qualitative methods that allow one to assess whether measured baseline covariates are balanced between treatment groups in the weighted sample. The quantitative methods use the weighted standardized difference to compare means, prevalences, higher‐order moments, and interactions. The qualitative methods employ graphical methods to compare the distribution of continuous baseline covariates between treated and control subjects in the weighted sample. Finally, we illustrate the application of these methods in an empirical case study. We propose a formal set of balance diagnostics that contribute towards an evolving concept of ‘best practice’ when using IPTW to estimate causal treatment effects using observational data. © 2015 The Authors. Statistics in Medicine Published by John Wiley & Sons Ltd.
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                Author and article information

                Journal
                Prev Med Rep
                Prev Med Rep
                Preventive Medicine Reports
                Published by Elsevier Inc.
                2211-3355
                25 June 2023
                25 June 2023
                : 102302
                Affiliations
                [a ]Department of Family and Community Medicine, Saint Louis University School of Medicine, 1008 S. Spring, St. Louis, MO. 63110 USA
                [b ]Advanced HEAlth Data (AHEAD) Research Institute, Saint Louis University School of Medicine, 3545 Lafayette Ave, 4 th Floor, St. Louis, MO. 63104 USA
                [c ]Saint Louis University, School of Medicine, Department of Internal Medicine, Division of Geriatric Medicine, Saint Louis University School of Medicine. 1402 South Grand Blvd, St. Louis MO. United States
                [d ]Saint Louis University, School of Medicine, Department of Internal Medicine Division of Infectious Diseases, Allergy, and Immunology, Saint Louis, MO. United States
                [e ]Saint Louis University, Department of Molecular Microbiology & Immunology, Saint Louis, MO. United States
                [f ]Department of Psychiatry and Behavioral Neuroscience, Saint Louis University School of Medicine, 1438 South Grand Blvd. St. Louis, MO 63104 USA
                Author notes
                [* ]Corresponding author.
                Article
                S2211-3355(23)00193-6 102302
                10.1016/j.pmedr.2023.102302
                10290736
                37441187
                fdc911a0-3298-4361-a9ad-ccded2392a05
                © 2023 Published by Elsevier Inc.

                Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active.

                History
                : 16 February 2023
                : 21 June 2023
                : 23 June 2023
                Categories
                Article

                vaccination,covid-19,prevention,epidemiology,observational cohort

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