start-ver=1.4 cd-journal=joma no-vol=33 cd-vols= no-issue=2 article-no= start-page=110 end-page=111 dt-received= dt-revised= dt-accepted= dt-pub-year=2023 dt-pub=20230205 dt-online= en-article= kn-article= en-subject= kn-subject= en-title= kn-title=Adverse Reactions and Attitudes Toward the BNT162b2 COVID-19 Vaccine in Children 5 to 11 Years of Age in Japan en-subtitle= kn-subtitle= en-abstract= kn-abstract= en-copyright= kn-copyright= en-aut-name=MatsumotoNaomi en-aut-sei=Matsumoto en-aut-mei=Naomi kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=1 ORCID= en-aut-name=ShimizuJunya en-aut-sei=Shimizu en-aut-mei=Junya kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=2 ORCID= en-aut-name=YokoyamaYuji en-aut-sei=Yokoyama en-aut-mei=Yuji kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=3 ORCID= en-aut-name=TsukaharaHirokazu en-aut-sei=Tsukahara en-aut-mei=Hirokazu kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=4 ORCID= en-aut-name=YorifujiTakashi en-aut-sei=Yorifuji en-aut-mei=Takashi kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=5 ORCID= affil-num=1 en-affil=Department of Epidemiology, Faculty of Medicine, Dentistry and Pharmaceutical Sciences, Okayama University kn-affil= affil-num=2 en-affil=Department of Pediatrics, National Hospital Organization, Okayama Medical Center kn-affil= affil-num=3 en-affil=Department of Pediatrics, Okayama Aiiku Clinic kn-affil= affil-num=4 en-affil=Department of Pediatrics, Faculty of Medicine, Dentistry and Pharmaceutical Sciences, Okayama University kn-affil= affil-num=5 en-affil=Department of Epidemiology, Faculty of Medicine, Dentistry and Pharmaceutical Sciences, Okayama University kn-affil= END start-ver=1.4 cd-journal=joma no-vol=32 cd-vols= no-issue=12 article-no= start-page=567 end-page=569 dt-received= dt-revised= dt-accepted= dt-pub-year=2022 dt-pub=20221205 dt-online= en-article= kn-article= en-subject= kn-subject= en-title= kn-title=Association Between Fever and Antibody Titer Trends After a Third Dose of the mRNA-1273 Vaccine en-subtitle= kn-subtitle= en-abstract= kn-abstract= en-copyright= kn-copyright= en-aut-name=MatsumotoNaomi en-aut-sei=Matsumoto en-aut-mei=Naomi kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=1 ORCID= en-aut-name=KadowakiTomoka en-aut-sei=Kadowaki en-aut-mei=Tomoka kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=2 ORCID= en-aut-name=MatsuoRumi en-aut-sei=Matsuo en-aut-mei=Rumi kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=3 ORCID= en-aut-name=SasakiAyako en-aut-sei=Sasaki en-aut-mei=Ayako kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=4 ORCID= en-aut-name=MiyajiChikara en-aut-sei=Miyaji en-aut-mei=Chikara kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=5 ORCID= en-aut-name=HiguchiChigusa en-aut-sei=Higuchi en-aut-mei=Chigusa kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=6 ORCID= en-aut-name=NakayamaMasanori en-aut-sei=Nakayama en-aut-mei=Masanori kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=7 ORCID= en-aut-name=SakuradaYasue en-aut-sei=Sakurada en-aut-mei=Yasue kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=8 ORCID= en-aut-name=HagiyaHideharu en-aut-sei=Hagiya en-aut-mei=Hideharu kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=9 ORCID= en-aut-name=TakaoSoshi en-aut-sei=Takao en-aut-mei=Soshi kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=10 ORCID= en-aut-name=OtsukaFumio en-aut-sei=Otsuka en-aut-mei=Fumio kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=11 ORCID= en-aut-name=YorifujiTakashi en-aut-sei=Yorifuji en-aut-mei=Takashi kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=12 ORCID= affil-num=1 en-affil=Department of Epidemiology, Faculty of Medicine, Dentistry and Pharmaceutical Sciences, Okayama University kn-affil= affil-num=2 en-affil=Department of Epidemiology, Okayama University Graduate School of Medicine, Dentistry, and Pharmaceutical Sciences kn-affil= affil-num=3 en-affil=Department of Epidemiology, Okayama University Graduate School of Medicine, Dentistry, and Pharmaceutical Sciences kn-affil= affil-num=4 en-affil=Department of Epidemiology, Faculty of Medicine, Dentistry and Pharmaceutical Sciences, Okayama University kn-affil= affil-num=5 en-affil=Okayama University Health Service Center kn-affil= affil-num=6 en-affil=Okayama University Health Service Center kn-affil= affil-num=7 en-affil=Office of Innovative Medicine, Organization for Research Strategy and Development, Okayama University kn-affil= affil-num=8 en-affil=Department of General Medicine, Okayama University Graduate School of Medicine, Dentistry, and Pharmaceutical Sciences kn-affil= affil-num=9 en-affil=Department of General Medicine, Okayama University Graduate School of Medicine, Dentistry, and Pharmaceutical Sciences kn-affil= affil-num=10 en-affil=Department of Epidemiology, Faculty of Medicine, Dentistry and Pharmaceutical Sciences, Okayama University kn-affil= affil-num=11 en-affil=Department of General Medicine, Okayama University Graduate School of Medicine, Dentistry, and Pharmaceutical Sciences kn-affil= affil-num=12 en-affil=Department of Epidemiology, Faculty of Medicine, Dentistry and Pharmaceutical Sciences, Okayama University kn-affil= en-keyword=SARS-CoV-2 kn-keyword=SARS-CoV-2 en-keyword=mRNA-1273 kn-keyword=mRNA-1273 en-keyword=antibody kn-keyword=antibody en-keyword=reactogenicity kn-keyword=reactogenicity en-keyword=adverse reaction kn-keyword=adverse reaction END start-ver=1.4 cd-journal=joma no-vol=30 cd-vols= no-issue=4 article-no= start-page=153 end-page=162 dt-received= dt-revised= dt-accepted= dt-pub-year=2020 dt-pub=20200405 dt-online= en-article= kn-article= en-subject= kn-subject= en-title= kn-title=Causal Diagrams: Pitfalls and Tips en-subtitle= kn-subtitle= en-abstract= kn-abstract=Graphical models are useful tools in causal inference, and causal directed acyclic graphs (DAGs) are used extensively to determine the variables for which it is sufficient to control for confounding to estimate causal effects. We discuss the following ten pitfalls and tips that are easily overlooked when using DAGs: 1) Each node on DAGs corresponds to a random variable and not its realized values; 2) The presence or absence of arrows in DAGs corresponds to the presence or absence of individual causal effect in the population; 3) "Non-manipulable" variables and their arrows should be drawn with care; 4) It is preferable to draw DAGs for the total population, rather than for the exposed or unexposed groups; 5) DAGs are primarily useful to examine the presence of confounding in distribution in the notion of confounding in expectation; 6) Although DAGs provide qualitative differences of causal structures, they cannot describe details of how to adjust for confounding; 7) DAGs can be used to illustrate the consequences of matching and the appropriate handling of matched variables in cohort and case-control studies; 8) When explicitly accounting for temporal order in DAGs, it is necessary to use separate nodes for each timing; 9) In certain cases, DAGs with signed edges can be used in drawing conclusions about the direction of bias; and 10) DAGs can be (and should be) used to describe not only confounding bias but also other forms of bias. We also discuss recent developments of graphical models and their future directions. en-copyright= kn-copyright= en-aut-name=SuzukiEtsuji en-aut-sei=Suzuki en-aut-mei=Etsuji kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=1 ORCID= en-aut-name=ShinozakiTomohiro en-aut-sei=Shinozaki en-aut-mei=Tomohiro kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=2 ORCID= en-aut-name=YamamotoEiji en-aut-sei=Yamamoto en-aut-mei=Eiji kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=3 ORCID= affil-num=1 en-affil=Department of Epidemiology, Graduate School of Medicine, Dentistry and Pharmaceutical Sciences, Okayama University kn-affil= affil-num=2 en-affil=Department of Information and Computer Technology, Faculty of Engineering, Tokyo University of Science kn-affil= affil-num=3 en-affil=Okayama University of Science kn-affil= en-keyword=bias kn-keyword=bias en-keyword=causal inference kn-keyword=causal inference en-keyword=causality kn-keyword=causality en-keyword=confounding kn-keyword=confounding en-keyword=directed acyclic graphs kn-keyword=directed acyclic graphs END start-ver=1.4 cd-journal=joma no-vol=30 cd-vols= no-issue=1 article-no= start-page=12 end-page=14 dt-received= dt-revised= dt-accepted= dt-pub-year=2020 dt-pub=20200105 dt-online= en-article= kn-article= en-subject= kn-subject= en-title= kn-title=Lessons From an Early-stage Epidemiological Study of Minamata Disease en-subtitle= kn-subtitle= en-abstract= kn-abstract= en-copyright= kn-copyright= en-aut-name=YorifujiTakashi en-aut-sei=Yorifuji en-aut-mei=Takashi kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=1 ORCID= affil-num=1 en-affil=Department of Epidemiology, Graduate School of Medicine, Dentistry and Pharmaceutical Sciences, Okayama University kn-affil= en-keyword=environment and public health kn-keyword=environment and public health en-keyword=epidemiology kn-keyword=epidemiology en-keyword=food contamination kn-keyword=food contamination en-keyword=methylmercury compounds kn-keyword=methylmercury compounds en-keyword=Minamata disease kn-keyword=Minamata disease END start-ver=1.4 cd-journal=joma no-vol=29 cd-vols= no-issue=4 article-no= start-page=123 end-page=124 dt-received= dt-revised= dt-accepted= dt-pub-year=2018 dt-pub=20180915 dt-online= en-article= kn-article= en-subject= kn-subject= en-title= kn-title=Long-term Trends in Prevalence of Neural Tube Defects in Japan en-subtitle= kn-subtitle= en-abstract= kn-abstract= en-copyright= kn-copyright= en-aut-name=YorifujiTakashi en-aut-sei=Yorifuji en-aut-mei=Takashi kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=1 ORCID= affil-num=1 en-affil=Department of Human Ecology, Graduate School of Environmental and Life Science, Okayama University kn-affil= en-keyword=dietary supplements kn-keyword=dietary supplements en-keyword= folic acid kn-keyword= folic acid en-keyword=fortification of the food supply kn-keyword=fortification of the food supply en-keyword=long-term trend kn-keyword=long-term trend en-keyword=neural tube defects kn-keyword=neural tube defects en-keyword=spina bifida kn-keyword=spina bifida END