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Bozsonyi K, Lester D, Zonda T, Bálint L, Veres E. A Population-Level Study Concerning the Assumed Association Between Suicide Rates and Antidepressant Consumption in Hungary. Omega (Westport) 2024; 89:122-137. [PMID: 35094585 DOI: 10.1177/00302228211067031] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/21/2024]
Abstract
BACKGROUND It has been claimed that the advent of modern antidepressants has reduced the suicide rate. AIMS To examine the correlation between the suicide rate and the prescription of antidepressants. METHOD A dynamic regression was employed to analyze a 73-month-long, monthly time series between 2010 and 2016 in Hungary. The independent variable was the Defined Daily Dose value for the number of antidepressant (AD) prescriptions filled each month. RESULTS The models failed to show a significant association between the prescription of antidepressants and age- and sex-specific monthly suicide rates. CONCLUSIONS The prescription of antidepressants in Hungary has had no impact on suicide rates.
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Affiliation(s)
| | | | - Tamás Zonda
- Hungarian Association for Suicide Prevention, Budapest, Hungary
| | - Lajos Bálint
- Népességtudományi Kutatóinézet, Budapest, Hungary
| | - Előd Veres
- Országos Kórházi Főigazgatóság, Budapest, Hungary
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Li Z, Huang Y, Patil D, Rubin M, Sanda MG. Covariate-specific evaluation of continuous biomarker. Stat Med 2023; 42:953-969. [PMID: 36600184 PMCID: PMC10071998 DOI: 10.1002/sim.9652] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/07/2022] [Revised: 10/14/2022] [Accepted: 12/28/2022] [Indexed: 01/06/2023]
Abstract
Diagnostic tests usually need to operate at a high sensitivity or specificity level in practice. Accordingly, specificity at the controlled sensitivity, or vice versa, is a clinically sensible performance metric for evaluating continuous biomarkers. Meanwhile, the performance of a biomarker may vary across sub-populations as defined by covariates, and covariate-specific evaluation can be informative. In this article, we develop a novel modeling and estimation method for covariate-specific specificity at a controlled sensitivity level. Unlike existing methods which typically adopt elaborate models of covariate effects over the entire biomarker distribution, our approach models covariate effects locally at a specific sensitivity level of interest. We also extend our proposed model to handle the whole continuum of sensitivities via dynamic regression and derive covariate-specific ROC curves. We provide the variance estimation through bootstrapping. The asymptotic properties are established. We conduct extensive simulation studies to evaluate the performance of our proposed methods in comparison with existing methods, and further illustrate the applications in two clinical studies for aggressive prostate cancer.
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Affiliation(s)
- Ziyi Li
- Department of Biostatistics, The University of Texas at MD Anderson Cancer Center, Houston, Texas, USA
| | - Yijian Huang
- Department of Biostatistics and Bioinformatics, Emory University, Atlanta, Georgia, USA
| | | | - Mark Rubin
- Department for BioMedical Research, Bern Center for Precision Medicine, University of Bern, Bern, Switzerland
| | - Martin G Sanda
- Department of Urology, Emory University, Atlanta, Georgia, USA
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Rees EE, Avery BP, Carabin H, Carson CA, Champredon D, de Montigny S, Dougherty B, Nasri BR, Ogden NH. Effectiveness of non-pharmaceutical interventions to reduce SARS-CoV-2 transmission in Canada and their association with COVID-19 hospitalization rates. Can Commun Dis Rep 2022; 48:438-448. [PMID: 38162959 PMCID: PMC10756332] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Subscribe] [Scholar Register] [Indexed: 01/03/2024]
Abstract
Background Non-pharmaceutical interventions (NPIs) aim to reduce the incidence of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infections mostly by limiting contacts between people where virus transmission can occur. However, NPIs limit social interactions and have negative impacts on economic, physical, mental and social well-being. It is, therefore, important to assess the impact of NPIs on reducing the number of coronavirus disease 2019 (COVID-19) cases and hospitalizations to justify their use. Methods Dynamic regression models accounting for autocorrelation in time series data were used with data from six Canadian provinces (British Columbia, Alberta, Saskatchewan, Manitoba, Ontario, Québec) to assess 1) the effect of NPIs (measured using a stringency index) on SARS-CoV-2 transmission (measured by the effective reproduction number), and 2) the effect of the number of hospitalized COVID-19 patients on the stringency index. Results Increasing stringency index was associated with a statistically significant decrease in the transmission of SARS-CoV-2 in Alberta, Saskatchewan, Manitoba, Ontario and Québec. The effect of stringency on transmission was time-lagged in all of these provinces except for Ontario. In all provinces except for Saskatchewan, increasing hospitalization rates were associated with a statistically significant increase in the stringency index. The effect of hospitalization on stringency was time-lagged. Conclusion These results suggest that NPIs have been effective in Canadian provinces, and that their implementation has been, in part, a response to increasing hospitalization rates of COVID-19 patients.
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Affiliation(s)
- Erin E Rees
- Public Health Risk Sciences Division, National Microbiology Laboratory (PHRSD), Public Health Agency of Canada, Saint-Hyacinthe, QC and Guelph, ON
- Groupe de recherche en épidémiologie des zoonoses et santé publique (GREZOSP), Faculty of Veterinary Medicine, Université de Montréal, Saint-Hyacinthe, QC
- Centre de recherche en santé publique (CReSP), Université de Montréal, Montréal, QC
| | - Brent P Avery
- Food-borne Disease and Antimicrobial Resistance Surveillance Division, Centre for Food-borne and Environmental and Zoonotic Infectious Diseases, Public Health Agency of Canada, Guelph, ON
| | - Hélène Carabin
- Groupe de recherche en épidémiologie des zoonoses et santé publique (GREZOSP), Faculty of Veterinary Medicine, Université de Montréal, Saint-Hyacinthe, QC
- Centre de recherche en santé publique (CReSP), Université de Montréal, Montréal, QC
- Faculty of Veterinary Medicine, Université de Montréal, Montréal, QC
| | - Carolee A Carson
- Food-borne Disease and Antimicrobial Resistance Surveillance Division, Centre for Food-borne and Environmental and Zoonotic Infectious Diseases, Public Health Agency of Canada, Guelph, ON
| | - David Champredon
- Public Health Risk Sciences Division, National Microbiology Laboratory (PHRSD), Public Health Agency of Canada, Saint-Hyacinthe, QC and Guelph, ON
| | - Simon de Montigny
- Groupe de recherche en épidémiologie des zoonoses et santé publique (GREZOSP), Faculty of Veterinary Medicine, Université de Montréal, Saint-Hyacinthe, QC
- School of Public Health, Université de Montréal, Montréal, QC
- Centre de recherche du CHU Sainte-Justine, Université de Montréal, Montréal, QC
| | - Brendan Dougherty
- Food-borne Disease and Antimicrobial Resistance Surveillance Division, Centre for Food-borne and Environmental and Zoonotic Infectious Diseases, Public Health Agency of Canada, Guelph, ON
| | - Bouchra R Nasri
- Centre de recherche en santé publique (CReSP), Université de Montréal, Montréal, QC
- School of Public Health, Université de Montréal, Montréal, QC
| | - Nicholas H Ogden
- Public Health Risk Sciences Division, National Microbiology Laboratory (PHRSD), Public Health Agency of Canada, Saint-Hyacinthe, QC and Guelph, ON
- Groupe de recherche en épidémiologie des zoonoses et santé publique (GREZOSP), Faculty of Veterinary Medicine, Université de Montréal, Saint-Hyacinthe, QC
- Centre de recherche en santé publique (CReSP), Université de Montréal, Montréal, QC
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Jin J, Song X, Sun L. Dynamic semiparametric transformation models for recurrent event data with a terminal event. Stat Med 2022; 41:5432-5447. [PMID: 36121319 DOI: 10.1002/sim.9577] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/26/2021] [Revised: 08/01/2022] [Accepted: 09/05/2022] [Indexed: 11/06/2022]
Abstract
Recurrent event data with a terminal event commonly arise in many longitudinal follow-up studies. This article proposes a class of dynamic semiparametric transformation models for the marginal mean functions of the recurrent events with a terminal event, where some covariate effects may be time-varying. An estimation procedure is developed for the model parameters, and the asymptotic properties of the resulting estimators are established. In addition, relevant significance tests are suggested for examining whether or not covariate effects vary with time, and a model checking procedure is presented for assessing the adequacy of the proposed models. The finite sample performance of the proposed estimators is examined through simulation studies, and an application to a medical cost study of chronic heart failure patients is provided.
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Affiliation(s)
- Jin Jin
- School of Mathematics and Physics, University of Science and Technology Beijing, Beijing, China
| | - Xinyuan Song
- Department of Statistics, The Chinese University of Hong Kong, Hong Kong, China
| | - Liuquan Sun
- Institute of Applied Mathematics, Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing, China.,School of Mathematical Sciences, University of Chinese Academy of Sciences, Beijing, China
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Kabir F, Yu N, Yao W, Wu L, Jiang JH, Gu Y, Su H. Impact of aerosols on reservoir inflow: A case study for Big Creek Hydroelectric System in California. Hydrol Process 2018; 32:3365-3390. [PMID: 31073260 PMCID: PMC6501612 DOI: 10.1002/hyp.13265] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/24/2018] [Accepted: 08/07/2018] [Indexed: 06/09/2023]
Abstract
Accurate and reliable reservoir inflow forecast is instrumental to the efficient operation of the hydroelectric power systems. It has been discovered that natural and anthropogenic aerosols have a great influence on meteorological variables such as temperature, snow water equivalent, and precipitation, which in turn impact the reservoir inflow. Therefore, it is imperative for us to quantify the impact of aerosols on reservoir inflow and to incorporate the aerosol models into future reservoir inflow forecasting models. In this paper, a comprehensive framework was developed to quantify the impact of aerosols on reservoir inflow by integrating the Weather Research and Forecasting model with Chemistry (WRF-Chem) and a dynamic regression model. The statistical dynamic regression model produces forecasts for reservoir inflow based on the meteorological output variables from the WRF-Chem model. The case study was performed on the Florence Lake and Lake Thomas Alva Edison of the Big Creek Hydroelectric Project in the San Joaquin Region. The simulation results show that the presence of aerosols results in a significant reduction of annual reservoir inflow by 4-14%. In the summer, aerosols reduce precipitation, snow water equivalent, and snowmelt that leads to a reduction in inflow by 11-26%. In the spring, aerosols increase temperature and snowmelt which leads to an increase in inflow by 0.6-2%. Aerosols significantly reduce the amount of inflow in the summer when the marginal value of water is extremely high and slightly increase the inflow in the spring when the run-off risk is high. In summary, the presence of aerosols is detrimental to the optimal utilization of hydroelectric power systems.
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Affiliation(s)
- Farzana Kabir
- Electrical and Computer Engineering, University of California, Riverside, Riverside, California
| | - Nanpeng Yu
- Electrical and Computer Engineering, University of California, Riverside, Riverside, California
| | - Weixin Yao
- Department of Statistics, University of California, Riverside, Riverside, California
| | - Longtao Wu
- Jet Propulsion Laboratory, California Institute of Technology, Pasadena, California
| | - Jonathan H. Jiang
- Jet Propulsion Laboratory, California Institute of Technology, Pasadena, California
| | - Yu Gu
- Joint Institute for Regional Earth System Science and Engineering and Department of Atmospheric and Oceanic Science, University of California, Los Angeles, Los Angeles, California
| | - Hui Su
- Jet Propulsion Laboratory, California Institute of Technology, Pasadena, California
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