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Bai X, Zhou Z, Zheng Z, Li Y, Liu K, Zheng Y, Yang H, Zhu H, Chen S, Pan H. Development and evaluation of machine learning models for predicting large-for-gestational-age newborns in women exposed to radiation prior to pregnancy. BMC Med Inform Decis Mak 2024; 24:174. [PMID: 38902714 PMCID: PMC11188254 DOI: 10.1186/s12911-024-02556-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/17/2023] [Accepted: 05/28/2024] [Indexed: 06/22/2024] Open
Abstract
INTRODUCTION The correlation between radiation exposure before pregnancy and abnormal birth weight has been previously proven. However, for large-for-gestational-age (LGA) babies in women exposed to radiation before becoming pregnant, there is no prediction model yet. MATERIAL AND METHODS The data were collected from the National Free Preconception Health Examination Project in China. A sum of 455 neonates (42 SGA births and 423 non-LGA births) were included. A training set (n = 319) and a test set (n = 136) were created from the dataset at random. To develop prediction models for LGA neonates, conventional logistic regression (LR) method and six machine learning methods were used in this study. Recursive feature elimination approach was performed by choosing 10 features which made a big contribution to the prediction models. And the Shapley Additive Explanation model was applied to interpret the most important characteristics that affected forecast outputs. RESULTS The random forest (RF) model had the highest average area under the receiver-operating-characteristic curve (AUC) for predicting LGA in the test set (0.843, 95% confidence interval [CI]: 0.714-0.974). Except for the logistic regression model (AUC: 0.603, 95%CI: 0.440-0.767), other models' AUCs displayed well. Thereinto, the RF algorithm's final prediction model using 10 characteristics achieved an average AUC of 0.821 (95% CI: 0.693-0.949). CONCLUSION The prediction model based on machine learning might be a promising tool for the prenatal prediction of LGA births in women with radiation exposure before pregnancy.
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Affiliation(s)
- Xi Bai
- Key Laboratory of Endocrine Glucose & Lipids Metabolism and Brain Aging, Department of Endocrinology, Ministry of Education, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, Shandong, China
- Key Laboratory of Endocrinology of National Health Commission, Department of Endocrinology, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Science and Peking Union Medical College, Beijing, 100730, China
| | - Zhibo Zhou
- Key Laboratory of Endocrinology of National Health Commission, Department of Endocrinology, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Science and Peking Union Medical College, Beijing, 100730, China
| | - Zeyan Zheng
- Key Laboratory of Endocrinology of National Health Commission, Department of Endocrinology, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Science and Peking Union Medical College, Beijing, 100730, China
| | - Yansheng Li
- DHC Mediway Technology CO., Ltd, Beijing, China
| | - Kejia Liu
- DHC Mediway Technology CO., Ltd, Beijing, China
| | | | - Hongbo Yang
- Key Laboratory of Endocrinology of National Health Commission, Department of Endocrinology, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Science and Peking Union Medical College, Beijing, 100730, China
| | - Huijuan Zhu
- Key Laboratory of Endocrinology of National Health Commission, Department of Endocrinology, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Science and Peking Union Medical College, Beijing, 100730, China
| | - Shi Chen
- Key Laboratory of Endocrinology of National Health Commission, Department of Endocrinology, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Science and Peking Union Medical College, Beijing, 100730, China.
| | - Hui Pan
- Key Laboratory of Endocrinology of National Health Commission, Department of Endocrinology, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Science and Peking Union Medical College, Beijing, 100730, China.
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Zhao L, Wang S, Liu M, Cao Z, Xiao Y, Wang P, Jiangcuo Z, Jian W, Zhang Y, Xu R, Wang X, Peng W. Maternal urinary metal(loid)s and risk of preterm birth: A cohort study in the Tibetan Plateau. ENVIRONMENTAL POLLUTION (BARKING, ESSEX : 1987) 2023; 333:122085. [PMID: 37348700 DOI: 10.1016/j.envpol.2023.122085] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/26/2022] [Revised: 06/18/2023] [Accepted: 06/19/2023] [Indexed: 06/24/2023]
Abstract
Maternal metal(loid)s exposure has been related to preterm birth (PTB), but the results are still inconclusive. Previous studies have mainly discussed the harmful metal(loid)s, neglecting beneficial ones. We examined the association of maternal metal(loid)s with PTB and gestational age (GA) in a birth cohort from the Tibetan Plateau. We measured 29 metal(loid)s in urine samples from 1081 pregnant women in the third trimester. Information regarding demographics, socioeconomic status, diet, medication, and lifestyle was collected through standardized interviews. The associations of single metal(loid)s with PTB or GA were evaluated using a generalized linear mixed-effects model or linear mixed-effects model. Elastic net and Bayesian kernel machine regressions were used to explore the joint associations. Magnesium (Mg), Copper (Cu), and Tin (Sn) were the main "harmful" metal(loid)s positively and negatively associated with PTB or GA, respectively. Mg was the dominant "harmful" metal(loid)s associated with PTB in a J-shape. A one-fold increase in Mg was associated with a 38% increased risk of PTB [OR (95% CI) = 1.38 (1.15, 1.65), PFDR<0.05] and 0.17 weeks shortening of GA [β (95% CI) = -0.25 (-0.35, -0.14), PFDR<0.05]. Cesium (Cs), rubidium (Rb), and Molybdenum (Mo) were the main "beneficial" metals. Cs dominated the "beneficial" associations and was negatively associated with PTB in a linear manner. A one-fold increase in Cs was associated with a 67% decreased risk of PTB [OR (95% CI) = 0.43 (0.27, 0.67), PFDR<0.05] and 0.24 weeks of prolonged GA [β (95% CI) = 0.35 (0.13, 0.56), PFDR<0.05]. Ethnicity and living altitude modified the association of Mg and Cu with PTB or GA. In conclusion, Maternal urinary metal(loid)s were bi-directionally associated with PTB in a population in the Tibetan Plateau. Mg and Cs were the dominant "harmful" and "beneficial" metal(loid)s, respectively.
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Affiliation(s)
- Lei Zhao
- Department of Public Health, Medical College, Qinghai University, Xining, Qinghai, China
| | - Shulin Wang
- Department of Public Health, Medical College, Qinghai University, Xining, Qinghai, China
| | - Miao Liu
- Department of Epidemiology, School of Public Health (Shenzhen), Sun Yat-Sen University, Shenzhen, China
| | - Zhongqiang Cao
- Institute of Maternal and Child Health, Wuhan Children's Hospital (Wuhan Maternal and Child Healthcare Hospital), Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China
| | - Yuancan Xiao
- Qinghai Provincial Key Laboratory of Tibetan Medicine Pharmacology and Safety Evaluation, Northwest Institute of Plateau Biology, Chinese Academy of Sciences, Xining, Qinghai, China
| | - Pinhua Wang
- Department of Obstetrics and Gynecology, Qinghai Red Cross Hospital, Xining, Qinghai, China
| | | | - Wenxiu Jian
- Department of Public Health, Medical College, Qinghai University, Xining, Qinghai, China
| | - Yangrui Zhang
- Department of Public Health, Medical College, Qinghai University, Xining, Qinghai, China
| | - Ruihua Xu
- Department of Public Health, Medical College, Qinghai University, Xining, Qinghai, China
| | - Xuejun Wang
- Department of Anesthesiology, Qinghai Red Cross Hospital, Xining, Qinghai, China
| | - Wen Peng
- Department of Public Health, Medical College, Qinghai University, Xining, Qinghai, China.
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Hayama SI, Nakanishi S, Tanaka A, Konno F, Kawamoto Y, Omi T. Influence of radiation exposure to delayed fetal growth in wild Japanese monkeys after the Fukushima accident. Front Vet Sci 2023; 10:1151361. [PMID: 37180055 PMCID: PMC10169707 DOI: 10.3389/fvets.2023.1151361] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/26/2023] [Accepted: 04/13/2023] [Indexed: 05/15/2023] Open
Abstract
Wild Japanese monkeys (Macaca fuscata) were exposed to radiation after the Fukushima Daiichi nuclear accident in 2011. To clarify the biological effects of radiation exposure on their fetal growth, pregnant monkeys and their fetuses were analyzed. These animals were collected between 2008 and 2020 (before and after the accident in 2011) living in Fukushima City, approximately 70 km from the nuclear power plant. Multiple regression analyses were conducted with fetal body weight (FBW) and fetal head circumference (FHS) as objective variables, and maternal and fetal factors as explanatory variables. The maternal factors were relative exposure dose rate (REDR), age, body weight, body length, fat index, and parity. The fetal factors were crown ramp length (CRL) and sex. Multiple regression analyses showed that FBR and FHS growth were positively associated with CRL, maternal body length, and negatively associated with REDR. Since the relative growth of FBR and FHS to CRL decreased with increasing REDR, radiation exposure due to the nuclear accident may have contributed to the delayed fetal growth observed in Japanese monkeys.
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Affiliation(s)
- Shin-ichi Hayama
- School of Veterinary Medicine, Nippon Veterinary and Life Science University, Musashino, Tokyo, Japan
| | - Setsuko Nakanishi
- School of Veterinary Medicine, Nippon Veterinary and Life Science University, Musashino, Tokyo, Japan
| | - Aki Tanaka
- School of Veterinary Medicine, Nippon Veterinary and Life Science University, Musashino, Tokyo, Japan
| | - Fumiharu Konno
- Tohoku Wildlife Management Center, Sendai, Miyagi, Japan
| | - Yoshi Kawamoto
- School of Veterinary Medicine, Nippon Veterinary and Life Science University, Musashino, Tokyo, Japan
| | - Toshinori Omi
- School of Veterinary Nursing and Technology, Nippon Veterinary and Life Science University, Musashino, Tokyo, Japan
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Low-dose ionizing radiation and adverse birth outcomes: a systematic review and meta-analysis. Int Arch Occup Environ Health 2023; 96:77-92. [PMID: 35913560 PMCID: PMC9823032 DOI: 10.1007/s00420-022-01911-2] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2022] [Accepted: 07/05/2022] [Indexed: 01/11/2023]
Abstract
OBJECTIVE Ionizing radiation is a human carcinogen, and there is evidence that exposure to low-dose ionizing radiation increases the risk of adverse birth outcomes. METHODS We undertook a systematic review and meta-analysis to synthesize the research of maternal and paternal exposure to low-dose radiation on low birth weight, miscarriage, pre-term delivery, and stillbirth. Our literature search used four databases (PubMed, Environmental Index, GeoBASE, and the Cumulative Index to Nursing and Allied Health Literature). We included study populations exposed to occupational and medical sources of radiation, nuclear disasters, and those living near nuclear power plants. We considered papers published between January 1st, 1990, and June 30th, 2021. The quality of the studies was assessed, and we performed meta-analysis using random effects models to generate summary measures of association. Forest plots were created to assess the heterogeneity in these measures, and funnel plots were used to assess publication bias. RESULTS Overall, 26 studies were identified, and these yielded measures of association from 10, 11, and 8 studies for low birth weight, miscarriage, and stillbirth outcomes, respectively. It was not possible to perform meta-analyses for pre-term delivery due to a small number of studies. The meta-analysis summary relative risk (RR) of having a low-birth-weight infant among those ever exposed to radiation relative to those unexposed, after adjusting for publication bias, was 1.29 (95% CI 0.97-1.73). The corresponding risk estimates for miscarriage and stillbirth were 1.15 (95% CI 1.02-1.30), and 1.19 (95% CI 0.98-1.45), respectively. CONCLUSIONS Our findings suggest that ionizing radiation increases the risk of adverse birth outcomes. Future work should strive to provide data needed to better understand the shape of the exposure-response curve.
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Yasuda S, Okazaki K, Nakano H, Ishii K, Kyozuka H, Murata T, Fujimori K, Goto A, Yasumura S, Ota M, Hata K, Suzuki K, Nakai A, Ohira T, Ohto H, Kamiya K. Effects of External Radiation Exposure on Perinatal Outcomes in Pregnant Women After the Fukushima Daiichi Nuclear Power Plant Accident: the Fukushima Health Management Survey. J Epidemiol 2022; 32:S104-S114. [PMID: 36464294 PMCID: PMC9703922 DOI: 10.2188/jea.je20210252] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/05/2022] Open
Abstract
BACKGROUND This study aimed to investigate the effects of maternal exposure to external radiation on perinatal outcomes among women who experienced the Fukushima Daiichi Nuclear Disaster (FDND) using the Fukushima Health Management Survey (FHMS). METHODS Data from the Pregnancy and Birth Survey and Basic Survey in the FHMS were combined to analyze external maternal radiation exposure following the FDND, and the relationship between radiation dose and perinatal outcomes was analyzed using binomial logistic regression analysis. Missing dose data were supplemented using multiple imputation. RESULTS A total of 6,875 individuals responded to the survey. Congenital anomalies occurred in 2.9% of patients, low birth weight (LBW) in 7.6%, small for gestation age (SGA; <10th percentile) in 8.9%, and preterm birth in 4.1%. The median maternal external radiation dose was 0.5 mSv (maximum, 5.2 mSv). Doses were classified as follows: <1 mSv (reference), 1 to <2 mSv, and ≥2 mSv. For congenital anomalies, the crude odds ratio for 1 to <2 mSv was 0.81 (95% confidence interval [CI], 0.56-1.17) (no participants with congenital anomaly were exposed to ≥2 mSv). At 1 to <2 mSv and ≥2 mSv, the respective adjusted odds ratios were 0.91 (95% CI, 0.71-1.18) and 1.21 (95% CI, 0.53-2.79) for LBW, 1.14 (95% CI, 0.92-1.42) and 0.84 (95% CI, 0.30-2.37) for SGA, and 0.91 (95% CI, 0.65-1.29) and 1.05 (95% CI, 0.22-4.87) for preterm birth. CONCLUSION External radiation dose due to the FDND was not associated with congenital anomalies, LBW, SGA, or preterm birth.
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Affiliation(s)
- Shun Yasuda
- Department of Obstetrics and Gynecology, Fukushima Medical University School of Medicine, Fukushima, Japan,Radiation Medical Science Center for the Fukushima Health Management Survey, Fukushima Medical University, Fukushima, Japan
| | - Kanako Okazaki
- Radiation Medical Science Center for the Fukushima Health Management Survey, Fukushima Medical University, Fukushima, Japan,Department of Physical Therapy, Fukushima Medical University School of Health Sciences, Fukushima, Japan
| | - Hironori Nakano
- Radiation Medical Science Center for the Fukushima Health Management Survey, Fukushima Medical University, Fukushima, Japan,Department of Epidemiology, Fukushima Medical University School of Medicine, Fukushima, Japan
| | - Kayoko Ishii
- Radiation Medical Science Center for the Fukushima Health Management Survey, Fukushima Medical University, Fukushima, Japan
| | - Hyo Kyozuka
- Department of Obstetrics and Gynecology, Fukushima Medical University School of Medicine, Fukushima, Japan
| | - Tsuyoshi Murata
- Department of Obstetrics and Gynecology, Fukushima Medical University School of Medicine, Fukushima, Japan
| | - Keiya Fujimori
- Department of Obstetrics and Gynecology, Fukushima Medical University School of Medicine, Fukushima, Japan,Radiation Medical Science Center for the Fukushima Health Management Survey, Fukushima Medical University, Fukushima, Japan
| | - Aya Goto
- Radiation Medical Science Center for the Fukushima Health Management Survey, Fukushima Medical University, Fukushima, Japan,Center for Integrated Science and Humanities, Fukushima Medical University, Fukushima, Japan
| | - Seiji Yasumura
- Radiation Medical Science Center for the Fukushima Health Management Survey, Fukushima Medical University, Fukushima, Japan,Department of Public Health, Fukushima Medical University School of Medicine, Fukushima, Japan
| | - Misao Ota
- Radiation Medical Science Center for the Fukushima Health Management Survey, Fukushima Medical University, Fukushima, Japan,Department of Midwifery and Maternal Nursing, Fukushima Medical University School of Nursing, Fukushima, Japan
| | - Kenichi Hata
- Radiation Medical Science Center for the Fukushima Health Management Survey, Fukushima Medical University, Fukushima, Japan,Fukushima Society of Obstetrics and Gynecology, Fukushima, Japan
| | - Kohta Suzuki
- Radiation Medical Science Center for the Fukushima Health Management Survey, Fukushima Medical University, Fukushima, Japan,Department of Health and Psychosocial Medicine, Aichi Medical University School of Medicine, Aichi, Japan
| | - Akihito Nakai
- Radiation Medical Science Center for the Fukushima Health Management Survey, Fukushima Medical University, Fukushima, Japan,Nippon Medical School Tamanagayama Hospital, Tokyo, Japan
| | - Tetsuya Ohira
- Radiation Medical Science Center for the Fukushima Health Management Survey, Fukushima Medical University, Fukushima, Japan,Department of Epidemiology, Fukushima Medical University School of Medicine, Fukushima, Japan
| | - Hitoshi Ohto
- Radiation Medical Science Center for the Fukushima Health Management Survey, Fukushima Medical University, Fukushima, Japan
| | - Kenji Kamiya
- Radiation Medical Science Center for the Fukushima Health Management Survey, Fukushima Medical University, Fukushima, Japan,Research Institute for Radiation Biology and Medicine, Hiroshima University, Hiroshima, Japan
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Zhou F, Cheng T, Xing Y, Ma H, Yang L. Network exploration of gene signatures underlying low birth weight induced metabolic alterations. Medicine (Baltimore) 2022; 101:e31489. [PMID: 36316897 PMCID: PMC9622720 DOI: 10.1097/md.0000000000031489] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/02/2022] Open
Abstract
BACKGROUND This study explored underlying gene signatures of low birth weight (LBW) by analyzing differentially expressed genes (DEGs) between LBW and normal birth weight (NBW) subjects. METHODS Subjects with different birth weight was collected from GEO database. P < .05 and | logFC | ≥ 1.0 were used for screening DEGs. David (2021 Update) was used to perform GO annotation and KEGG signaling pathway enrichment analysis. The protein-protein interaction network of DEGs was constructed using the STRING database, in which hub genes were mined through Cytoscape software. RESULTS A total of 326 DEGs were identified, including 287 up-regulated genes and 39 down-regulated genes. The GO biological processes enriched by DEGs mainly involved epidermal growth, keratinization and intermediate fibrous tissue. The DEGs were significantly enriched in intracellular insoluble membranes, desmosomes and extracellular space. Their molecular functions mainly focused on structural molecular activity, structural components of epidermis and structural components of cytoskeleton. PI3K/AKT signaling pathway and tight junction were highlighted as critical pathways enriched by DEGs. Ten hub genes which included KRT14, EGF, DSP, DSG1, KRT16, KRT6A, EPCAM, SPRR1B, PKP1, and PPL were identified from the constructed protein-protein interaction network. CONCLUSION A total of 326 DEGs and 10 hub genes were identified as candidates for metabolic disorders in LBW individuals. Our results indicated PI3K/AKT signaling pathway as an intrauterine adaptive mechanism for LBW individuals. We observed activated PI3K/AKT pathway in LBW individuals, which would promote growth and development at the early stage of life, but adversely introduce extra metabolic stress and thereby potentially induce metabolic disorders in adulthood.
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Affiliation(s)
- Fei Zhou
- Department of Internal Medicine, Hebei Medical University, Shijiazhuang, Hebei, China
- Key Laboratory of Metabolic Diseases, Hebei General Hospital, Shijiazhuang, Hebei, China
| | - Tiantian Cheng
- Key Laboratory of Metabolic Diseases, Hebei General Hospital, Shijiazhuang, Hebei, China
| | - Yuling Xing
- Key Laboratory of Metabolic Diseases, Hebei General Hospital, Shijiazhuang, Hebei, China
| | - Huijuan Ma
- Department of Internal Medicine, Hebei Medical University, Shijiazhuang, Hebei, China
- Key Laboratory of Metabolic Diseases, Hebei General Hospital, Shijiazhuang, Hebei, China
- Department of Endocrinology, Hebei General Hospital, Shijiazhuang, Hebei, China
- *Correspondence: Huijuan Ma, Hebei Key Laboratory of Metabolic Diseases, Hebei General Hospital, (e-mail: )
| | - Linlin Yang
- Key Laboratory of Metabolic Diseases, Hebei General Hospital, Shijiazhuang, Hebei, China
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Development and Evaluation of a Machine Learning Prediction Model for Small-for-Gestational-Age Births in Women Exposed to Radiation before Pregnancy. J Pers Med 2022; 12:jpm12040550. [PMID: 35455666 PMCID: PMC9031835 DOI: 10.3390/jpm12040550] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/26/2022] [Revised: 03/15/2022] [Accepted: 03/16/2022] [Indexed: 12/22/2022] Open
Abstract
Exposure to radiation has been associated with increased risk of delivering small-for-gestational-age (SGA) newborns. There are no tools to predict SGA newborns in pregnant women exposed to radiation before pregnancy. Here, we aimed to develop an array of machine learning (ML) models to predict SGA newborns in women exposed to radiation before pregnancy. Patients’ data was obtained from the National Free Preconception Health Examination Project from 2010 to 2012. The data were randomly divided into a training dataset (n = 364) and a testing dataset (n = 91). Eight various ML models were compared for solving the binary classification of SGA prediction, followed by a post hoc explainability based on the SHAP model to identify and interpret the most important features that contribute to the prediction outcome. A total of 455 newborns were included, with the occurrence of 60 SGA births (13.2%). Overall, the model obtained by extreme gradient boosting (XGBoost) achieved the highest area under the receiver-operating-characteristic curve (AUC) in the testing set (0.844, 95% confidence interval (CI): 0.713–0.974). All models showed satisfied AUCs, except for the logistic regression model (AUC: 0.561, 95% CI: 0.355–0.768). After feature selection by recursive feature elimination (RFE), 15 features were included in the final prediction model using the XGBoost algorithm, with an AUC of 0.821 (95% CI: 0.650–0.993). ML algorithms can generate robust models to predict SGA newborns in pregnant women exposed to radiation before pregnancy, which may thus be used as a prediction tool for SGA newborns in high-risk pregnant women.
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Wollschläger D, Auvinen A, Blettner M, Zeeb H. Methodological considerations for interrupted time series analysis in radiation epidemiology: an overview. JOURNAL OF RADIOLOGICAL PROTECTION : OFFICIAL JOURNAL OF THE SOCIETY FOR RADIOLOGICAL PROTECTION 2021; 41:609-618. [PMID: 34261051 DOI: 10.1088/1361-6498/ac149c] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/01/2021] [Accepted: 07/14/2021] [Indexed: 06/13/2023]
Abstract
Interrupted time series analysis (ITSA) is a method that can be applied to evaluate health outcomes in populations exposed to ionizing radiation following major radiological events. Using aggregated time series data, ITSA evaluates whether the time trend of a health indicator shows a change associated with the radiological event. That is, ITSA checks whether there is a statistically significant discrepancy between the projection of a pre-event trend and the data empirically observed after the event. Conducting ITSA requires one to consider specific methodological issues due to unique threats to internal validity that make ITSA prone to bias. We here discuss the strengths and limitations of ITSA with respect to bias and confounding, data quality, and statistical aspects. We provide recommendations to strengthen the robustness of ITSA studies and reduce their susceptibility to producing spurious results as a consequence of arbitrary modelling decisions.
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Affiliation(s)
- Daniel Wollschläger
- Institute of Medical Biostatistics, Epidemiology and Informatics, University Medical Center of the Johannes Gutenberg-University Mainz, Mainz, Germany
| | - Anssi Auvinen
- Faculty of Social Sciences (Health Sciences), Tampere University, Tampere, Finland
- STUK - Radiation and Nuclear Safety Authority, Helsinki, Finland
| | - Maria Blettner
- Institute of Medical Biostatistics, Epidemiology and Informatics, University Medical Center of the Johannes Gutenberg-University Mainz, Mainz, Germany
| | - Hajo Zeeb
- Leibniz Institute for Prevention Research and Epidemiology-BIPS, Bremen, Germany
- Wissenschaftsschwerpunkt Gesundheitswissenschaften, Universität Bremen, Bremen, Germany
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Soleman SR, Fujitani T, Harada KH. Letter to the editor regarding "spatiotemporal association of low birth weight with Cs-137 deposition at the prefecture level in Japan after the Fukushima nuclear power plant accidents". Environ Health 2020; 19:121. [PMID: 33239011 PMCID: PMC7687688 DOI: 10.1186/s12940-020-00651-5] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/27/2020] [Accepted: 08/31/2020] [Indexed: 05/24/2023]
Abstract
In the previous report, association between increased low birth weight prevalence and radiocesium deposition after 2011 Fukushima nuclear disaster was found. However, the statistical analyses therein raised several questions. First, ecological variables are not justified enough to adjust potential confounding. Second, the spatiotemporal regression model does not consider temporal reduction in radiation dose rate. Third, dose-response plot between dose rates and odds ratios overestimates R2 and underestimates p value.
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Affiliation(s)
- Sani Rachman Soleman
- Department of Health and Environmental Sciences, Kyoto University Graduate School of Medicine, Yoshida Konoe, Sakyo, Kyoto, 6068501 Japan
- Department of Public Health, Faculty of Medicine, Universitas Islam Indonesia, Yogyakarta, 55584 Indonesia
| | - Tomoko Fujitani
- Department of Health and Environmental Sciences, Kyoto University Graduate School of Medicine, Yoshida Konoe, Sakyo, Kyoto, 6068501 Japan
| | - Kouji H. Harada
- Department of Health and Environmental Sciences, Kyoto University Graduate School of Medicine, Yoshida Konoe, Sakyo, Kyoto, 6068501 Japan
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Scherb H, Hayashi K. Response to the "Letter to the Editor" by Alfred Körblein, "Short term increase in low birthweight babies after Fukushima". Environ Health 2020; 19:125. [PMID: 33239024 PMCID: PMC7687820 DOI: 10.1186/s12940-020-00675-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2020] [Accepted: 11/02/2020] [Indexed: 06/11/2023]
Affiliation(s)
- Hagen Scherb
- Helmholtz Zentrum München, German Research Center for Environmental Health, Institute of Computational Biology, Ingolstädter Landstr. 1, 85764 Neuherberg, Germany
| | - Keiji Hayashi
- Hayashi Children’s Clinic, 4-6-11-1F Nagata, Joto-ku Osaka-Shi, Osaka, 536-0022 Japan
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Scherb H, Hayashi K. Response to the "letter to the editor" by Sani Rachman Soleman et al., "spatiotemporal association of low birth weight with Cs-137 deposition at the prefecture level in Japan after the Fukushima nuclear power plant accidents". Environ Health 2020; 19:123. [PMID: 33239051 PMCID: PMC7687987 DOI: 10.1186/s12940-020-00661-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/16/2020] [Accepted: 09/29/2020] [Indexed: 05/08/2023]
Abstract
We thank Sani Rachman Soleman et al. for three specific points of criticism concerning our investigation of the ecological association between low birth weight (LBW) and radioactive contamination in Japan after the Fukushima Daiichi Nuclear Power Plant (FDNPP) accidents: 1. Ecological variables are not justified enough to adjust potential confounding. 2. The spatiotemporal regression model does not consider temporal reduction in radiation dose rate. 3. Dose-response plot between dose rates and odds ratios overestimates R2 and underestimates p-value. This criticism is a good starting point to explain some of the technical backgrounds of our approach in more detail.
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Affiliation(s)
- Hagen Scherb
- Helmholtz Zentrum München, German Research Center for Environmental Health, Institute of Computational Biology, Ingolstädter Landstr. 1, D-85764 Neuherberg, Germany
| | - Keiji Hayashi
- Hayashi Children’s Clinic, 4-6-11-1F Nagata, Joto-ku Osaka-Shi, Osaka, 536-0022 Japan
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Körblein A. Short term increase in low birth weight babies after Fukushima. Environ Health 2020; 19:120. [PMID: 33239016 PMCID: PMC7687704 DOI: 10.1186/s12940-020-00650-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/23/2020] [Accepted: 08/31/2020] [Indexed: 06/11/2023]
Abstract
An analysis of low birth weight (LBW) births in ten contaminated prefectures of Japan, 1995-2018, finds a statistically significant increase in the LBW proportion in 2012-2013, but no increase after 2013. In the rest of Japan (37 prefectures), no increase in LBW births was found after the Fukushima accident.
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