15
views
0
recommends
+1 Recommend
0 collections
    0
    shares
      • Record: found
      • Abstract: found
      • Article: found
      Is Open Access

      The association between osteoporosis medications and lowered all-cause mortality after hip or vertebral fracture in older and oldest-old adults: a nationwide population-based study

      research-article

      Read this article at

      Bookmark
          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

          Abstract

          Background: Osteoporotic fracture is a common public-health problem in ageing societies. Although post-fracture usage of osteoporosis medications may reduce mortality, recent results have been inconsistent. We aimed to examine associations between osteoporosis medication and mortality in older adults, particularly oldest-old adults (>=85 years old).

          Methods: Participants aged 65 years old and older newly diagnosed with both osteoporosis and hip or vertebral fractures within 2009-2017 were recruited from the records of 23,455,164 people in Taiwan National Health Insurance Research Database (NHIRD). Osteoporosis medication exposure was calculated after the first-time ambulatory visit with newly diagnosed osteoporosis. Mortality and its specific causes were ascertained from Cause of Death Data. Patients were followed until death or censored at the end of 2018.

          Results: A total of 87,935 participants aged 65 years old and over (73.4% female), with a mean 4.13 follow-up years, were included. Taking medication was associated with significantly lower risk of mortality (hip fracture HR 0.75, vertebral fracture HR 0.74), even in the oldest-old adults (hip fracture HR 0.76, vertebral fracture HR 0.72), where a longer duration of taking osteoporosis medication was associated with lower all-cause mortality. Specific causes of mortality were also significantly lower for participants taking osteoporosis medication (cancer HR 0.84 in hip fracture, 0.75 in vertebral fracture; cardiovascular disease HR 0.85 in hip fracture, 0.91 in vertebral fracture).

          Conclusions: Osteoporosis medication after hip or vertebral fracture may reduce mortality risk in older adults, notably in oldest-old adults. Encouraging the use of post-fracture osteoporosis medication in healthcare policies is warranted.

          Related collections

          Most cited references37

          • Record: found
          • Abstract: found
          • Article: not found

          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.
            Bookmark
            • Record: found
            • Abstract: found
            • Article: not found

            Sensitivity Analysis in Observational Research: Introducing the E-Value.

            Sensitivity analysis is useful in assessing how robust an association is to potential unmeasured or uncontrolled confounding. This article introduces a new measure called the "E-value," which is related to the evidence for causality in observational studies that are potentially subject to confounding. The E-value is defined as the minimum strength of association, on the risk ratio scale, that an unmeasured confounder would need to have with both the treatment and the outcome to fully explain away a specific treatment-outcome association, conditional on the measured covariates. A large E-value implies that considerable unmeasured confounding would be needed to explain away an effect estimate. A small E-value implies little unmeasured confounding would be needed to explain away an effect estimate. The authors propose that in all observational studies intended to produce evidence for causality, the E-value be reported or some other sensitivity analysis be used. They suggest calculating the E-value for both the observed association estimate (after adjustments for measured confounders) and the limit of the confidence interval closest to the null. If this were to become standard practice, the ability of the scientific community to assess evidence from observational studies would improve considerably, and ultimately, science would be strengthened.
              Bookmark
              • Record: found
              • Abstract: found
              • Article: found
              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.
                Bookmark

                Author and article information

                Journal
                Aging (Albany NY)
                Aging
                Aging (Albany NY)
                Impact Journals
                1945-4589
                15 March 2022
                01 March 2022
                : 14
                : 5
                : 2239-2251
                Affiliations
                [1 ]Institute of Allied Health Sciences, College of Medicine, National Cheng Kung University, Tainan, Taiwan
                [2 ]Department of Family Medicine, College of Medicine, National Cheng Kung University, Tainan, Taiwan
                [3 ]International PhD Program in Biotech and Healthcare Management, College of Management, Taipei Medical University, Taipei, Taiwan
                [4 ]Clinical Data Center, Office of Data Science, Taipei Medical University, Taipei, Taiwan
                [5 ]Research Center of Data Science on Healthcare Industry, College of Management, Taipei Medical University, Taipei, Taiwan
                [6 ]Department of Public Health, College of Health Sciences, Kaohsiung Medical University, Kaohsiung, Taiwan
                [7 ]Department of Family Medicine, National Cheng Kung University Hospital, College of Medicine, National Cheng Kung University, Tainan, Taiwan
                [8 ]Institute of Gerontology, College of Medicine, National Cheng Kung University, Tainan, Taiwan
                [9 ]Clinical Big Data Research Center, Taipei Medical University Hospital, Taipei Medical University, Taipei, Taiwan
                Author notes
                [*]

                Equal contribution

                Correspondence to: Ching-Ju Chiu; email: cjchiu@mail.ncku.edu.tw
                Correspondence to: Chih-Hsing Wu; email: paulo@mail.ncku.edu.tw
                Article
                203927 203927
                10.18632/aging.203927
                8954959
                35232893
                9e3894e5-0643-4827-8daf-1ac136e7c687
                Copyright: © 2022 Li et al.

                This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

                History
                : 07 December 2021
                : 15 February 2022
                Categories
                Research Paper

                Cell biology
                osteoporosis,hip fracture,vertebral fracture,ageing,oldest-old adult
                Cell biology
                osteoporosis, hip fracture, vertebral fracture, ageing, oldest-old adult

                Comments

                Comment on this article