文献一覧: Sachiko Ono (著者)

4 0 0 0 OA Outpatient Prescriptions of Kampo Formulations in Japan

著者: Hayato Yamana Sachiko Ono Nobuaki Michihata Taisuke Jo Hideo Yasunaga
出版者: The Japanese Society of Internal Medicine
雑誌: Internal Medicine (ISSN:09182918)
巻号頁・発行日: pp.5012-20, (Released:2020-07-21)
参考文献数: 23
被引用文献数: 14

Objective Kampo is a traditional Japanese medicine using formulae of natural agents. Although Kampo is widely practiced, information regarding the current prescriptions of Kampo formulations is lacking. The aim of the study was to describe the outpatient use of Kampo formulations in the current Japanese health insurance system. Methods From the JMDC Claims Database, we identified subscribers with outpatient prescriptions of Kampo extract formulations between April 2017 and March 2018. Prescription records were summarized at the individual level to describe the pattern of each formula's use, such as the frequency of prescription and the number of days within a year that were covered by the prescriptions. We also examined whether or not Kampo formulations were prescribed in combination with other drugs. Results Of the 4.5 million subscribers, 13.5% received prescriptions of Kampo extracts within 1 year, and 54% of Kampo users were women. The most commonly prescribed Kampo formulae included kakkonto, shoseiryuto, and maoto, which were used for the short term covering a median of 5 to 7 days. There were also several formulae that were prescribed for longer periods. The median numbers of days covered by kamishoyosan and keishibukuryogan were 60 and 56, respectively. Kampo formulations were used in combination with Western drugs in 85% of prescriptions. Conclusion Kampo formulations are commonly prescribed under the Japanese insurance system and are frequently used in combination with Western drugs. The pattern of prescriptions varied across different formulae.

2020-10-20 11:26:59
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2 0 0 0 OA Machine learning-based models for outpatient prescription of Kampo formulations: an analysis of a health insurance claims database

著者: Hayato Yamana Akira Okada Sachiko Ono Nobuaki Michihata Taisuke Jo Hideo Yasunaga
出版者: Japan Epidemiological Association
雑誌: Journal of Epidemiology (ISSN:09175040)
巻号頁・発行日: pp.JE20220089, (Released:2023-01-14)
参考文献数: 15

Background: Despite the widespread practice of Japanese traditional Kampo medicine, the characteristics of patients receiving various Kampo formulations have not been documented in detail. We applied a machine learning model to a health insurance claims database to identify the factors associated with the use of Kampo formulations.Methods: A ten-percent sample of enrollees of the JMDC Claims Database in 2018 and 2019 was used to create the training and testing sets, respectively. Logistic regression with lasso regularization were performed in the training set to construct models with prescriptions of 10 commonly used Kampo formulations in one year as the dependent variable and data of the preceding year as independent variables. Models were applied to the testing set to calculate the C-statistics. Additionally, the performance of simplified scores using 10 or 5 variables were evaluated.Results: There were 338,924 and 399,174 enrollees in the training and testing sets, respectively. The commonly prescribed Kampo formulations included kakkonto, bakumondoto, and shoseityuto. Based on the lasso models, the C-statistics ranged from 0.643 (maoto) to 0.888 (tokishakuyakusan). The models identified both the common determinants of different Kampo formulations and the specific characteristics associated with particular Kampo formulations. The simplified scores were slightly inferior to full models.Conclusions: Lasso regression models showed good performance for explaining various Kampo prescriptions from claims data. The models identified the characteristics associated with Kampo formulation use.

2024-01-10 12:03:03
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2 0 0 0 OA Introduction to supervised machine learning in clinical epidemiology

著者: Sachiko Ono Tadahiro Goto
出版者: Society for Clinical Epidemiology
雑誌: Annals of Clinical Epidemiology (ISSN:24344338)
巻号頁・発行日: vol.4, no.3, pp.63-71, 2022 (Released:2022-07-01)
参考文献数: 46
被引用文献数: 5

Machine learning refers to a series of processes in which a computer finds rules from a vast amount of data. With recent advances in computer technology and the availability of a wide variety of health data, machine learning has rapidly developed and been applied in medical research. Currently, there are three types of machine learning: supervised, unsupervised, and reinforcement learning. In medical research, supervised learning is commonly used for diagnoses and prognoses, while unsupervised learning is used for phenotyping a disease, and reinforcement learning for maximizing favorable results, such as optimization of total patients’ waiting time in the emergency department. The present article focuses on the concept and application of supervised learning in medicine, the most commonly used machine learning approach in medicine, and provides a brief explanation of four algorithms widely used for prediction (random forests, gradient-boosted decision tree, support vector machine, and neural network). Among these algorithms, the neural network has further developed into deep learning algorithms to solve more complex tasks. Along with simple classification problems, deep learning is commonly used to process medical imaging, such as retinal fundus photographs for diabetic retinopathy diagnosis. Although machine learning can bring new insights into medicine by processing a vast amount of data that are often beyond human capacity, algorithms can also fail when domain knowledge is neglected. The combination of algorithms and human cognitive ability is a key to the successful application of machine learning in medicine.

2022-07-13 19:23:00
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2 0 0 0 OA Relationship Between Severe Respiratory Depression and Codeine-Containing Antitussives in Children: A Nested Case-Control Study

著者: Sachiko Ono Yosuke Ono Daisuke Koide Hideo Yasunaga
出版者: Japan Epidemiological Association
雑誌: Journal of Epidemiology (ISSN:09175040)
巻号頁・発行日: vol.30, no.3, pp.116-120, 2020-03-05 (Released:2020-03-05)
参考文献数: 19

Background: Guidelines recommend against all codeine use in children for its common indications of analgesia and cough suppression because of uncertain benefits and potential risk of death. However, because of its rarity, the occurrence of severe respiratory depression associated with codeine-containing antitussives has been poorly investigated. The objective of this study was to investigate the association between codeine-containing antitussives and severe respiratory depression in children.Methods: We retrospectively identified Japanese children who were prescribed antitussives for respiratory diseases from a large Japanese administrative claims database (JMDC, Tokyo, Japan). We collected data on baseline characteristics including age, sex, and comorbidity. Each case was matched with four controls with the same sex and age in the same year from the same type of medical institution. We then examined the association between codeine-containing antitussives and subsequent severe respiratory depression using multivariable conditional logistic regression analysis.Results: Of 164,047 children, 18,210 (11.1%) were prescribed codeine-containing antitussives. Of the children who took codeine-containing drugs, seven experienced severe respiratory depression. After adjusting for confounding factors, there was no significant difference in the proportion of severe respiratory depression between children with and without codeine-containing antitussives (odds ratio 1.15; 95% confidence interval, 0.48–2.78).Conclusion: Occurrence of respiratory depression was very rare, and the association of codeine with respiratory depression was insignificant, even in a large sample of children in Japan.

2020-03-30 09:11:59
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2 0 0 0 OA Relationship between severe respiratory depression and codeine-containing antitussives in children: a nested case-control study

著者: Sachiko Ono Yosuke Ono Daisuke Koide Hideo Yasunaga
出版者: Japan Epidemiological Association
雑誌: Journal of Epidemiology (ISSN:09175040)
巻号頁・発行日: pp.JE20180224, (Released:2019-03-02)
参考文献数: 19

Background: Guidelines recommend against all codeine use in children for its common indications of analgesia and cough suppression because of uncertain benefits and potential risk of death. However, because of its rarity, the occurrence of severe respiratory depression associated with codeine-containing antitussives has been poorly investigated. The objective of this study was to investigate the association between codeine-containing antitussives and severe respiratory depression in children.Methods: We retrospectively identified Japanese children who were prescribed antitussives for respiratory diseases from a large Japanese administrative claims database (JMDC, Tokyo, Japan). We collected data on baseline characteristics including age, sex, and comorbidity. Each case was matched with four controls with the same sex and age in the same year from the same type of medical institution. We then examined the association between codeine-containing antitussives and subsequent severe respiratory depression using a multivariable conditional logistic regression analysis.Results: Of 164,047 children, 18,210 (11.1%) were prescribed codeine-containing antitussives. Of the children who took codeine-containing drugs, seven experienced severe respiratory depression. After adjusting for confounding factors, there was no significant difference in the proportion of severe respiratory depression between children with and without codeine-containing antitussives (odds ratio: 1.15; 95% confidence interval: 0.48 - 2.78).Conclusion: Occurrence of respiratory depression was very rare, and the association of codeine with respiratory depression was insignificant, even in a large sample of children in Japan.

2019-04-20 23:07:05
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1 0 0 0 OA Association between Maoto Use and Hospitalization for Seasonal Influenza in a Nonelderly Cohort in Japan

著者: Hayato Yamana Sachiko Ono Nobuaki Michihata Taisuke Jo Hideo Yasunaga
出版者: The Japanese Society of Internal Medicine
雑誌: Internal Medicine (ISSN:09182918)
巻号頁・発行日: vol.60, no.21, pp.3401-3408, 2021-11-01 (Released:2021-11-01)
参考文献数: 29
被引用文献数: 4

Objective Maoto is a traditional Japanese Kampo formula used to treat influenza. However, clinical evidence for maoto has been limited to small-scale studies of its effect in alleviating symptoms. The present study evaluated whether or not the addition of maoto to a neuraminidase inhibitor was associated with a reduction in hospitalization following influenza. Methods Using the JMDC Claims Database, we identified outpatients <60 years old who were diagnosed with influenza by an antigen test from September 2013 to August 2018. One-to-five propensity score matching was conducted between patients who received maoto in addition to a neuraminidase inhibitor and those who received a neuraminidase inhibitor alone. Hospitalization within seven days of the influenza diagnosis was compared in the matched groups using the Mantel-Haenszel test. Results We identified 1.79 million cases of influenza from the database in the 5-year study period. Maoto was prescribed for 3.9% of the 1.67 million cases receiving a neuraminidase inhibitor. In the 64,613 propensity score-matched groups of patients, the 7-day hospitalization rate was 0.116% (n=75) for patients with maoto and 0.122% (n=394) for patients without maoto. The difference between these treatment groups was nonsignificant (common odds ratio, 0.95; 95% confidence interval, 0.74 to 1.22; p=0.695). Conclusion The addition of maoto to a neuraminidase inhibitor was not associated with a decrease in hospitalization among nonelderly patients with influenza. Further research is necessary to clarify the indication and efficacy of maoto.

2021-11-03 00:05:42
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1 0 0 0 OA Association between mandatory health examination attendance and diabetes treatment initiation among employees being treated for hypertension

著者: Hayato Yamana Sachiko Ono Akira Okada Taisuke Jo Hideo Yasunaga
出版者: Japan Society for Occupational Health
雑誌: Journal of Occupational Health (ISSN:13419145)
巻号頁・発行日: vol.62, no.1, pp.e12183, 2020 (Released:2021-01-25)
参考文献数: 17
被引用文献数: 3

Objectives: It is unclear whether mandatory health examination is effective for employees who are already being treated for chronic diseases. We focused on patients being treated for hypertension and evaluated the association between employer-based health examination attendance and diabetes treatment initiation.Methods: Using a database that stores health insurance claims and health examination results of subscribers enrolled in society-managed health insurance plans in Japan, we identified employees aged 40-59 years who were being treated for hypertension when starting diabetes treatment from April 2012 to December 2016. A case-crossover analysis was conducted using 90, 180, and 270 days prior to diabetes treatment initiation as reference points and 90 days after the mandatory health examination as the exposure period. We conducted a subgroup analysis by hemoglobin A1c (HbA1c) level and frequency of outpatient blood glucose testing before the mandatory health examination.Results: We identified 1464 individuals starting treatment for diabetes while on antihypertensive drugs. The overall odds ratio for starting diabetes treatment within 90 days of the health examination was 1.89 (95% confidence interval: 1.70-2.10). The subgroup analysis showed that this odds ratio increased as HbA1c level increased and as blood glucose testing frequency decreased.Conclusions: Among employees starting treatment for diabetes while being treated for hypertension, employer-based mandatory health examination attendance was associated with initiation of diabetes treatment. The health examinations may be functioning as a complement to screening in outpatient settings.

2021-05-19 23:37:06
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