Detecting geographical clusters of low birth weight and/or preterm birth in Japan

[1]  T. Sairenchi,et al.  LC-ESI/MS/MS analysis of neonicotinoids in urine of very low birth weight infants at birth , 2019, PloS one.

[2]  Gretchen A. Stevens,et al.  National, regional, and worldwide estimates of low birthweight in 2015, with trends from 2000: a systematic analysis , 2019, The Lancet. Global health.

[3]  H. Meltzer,et al.  Caffeine exposure during pregnancy, small for gestational age birth and neonatal outcome – results from the Norwegian Mother and Child Cohort Study , 2019, BMC Pregnancy and Childbirth.

[4]  Jun Zhang,et al.  Global, regional, and national estimates of levels of preterm birth in 2014: a systematic review and modelling analysis , 2018, The Lancet. Global health.

[5]  R. Kishi,et al.  Different Risk Factors for Very Low Birth Weight, Term-Small-for-Gestational-Age, or Preterm Birth in Japan , 2018, International journal of environmental research and public health.

[6]  I. van Kamp,et al.  WHO Environmental Noise Guidelines for the European Region: A Systematic Review of Transport Noise Interventions and Their Impacts on Health , 2017, International journal of environmental research and public health.

[7]  E. Ota,et al.  Japanese secular trends in birthweight and the prevalence of low birthweight infants during the last three decades: A population-based study , 2016, Scientific Reports.

[8]  T. Talbot,et al.  Identifying areas at risk of low birth weight using spatial epidemiology: A small area surveillance study. , 2016, Preventive medicine.

[9]  Shinji Nakaoka,et al.  New algorithm for constructing area-based index with geographical heterogeneities and variable selection: An application to gastric cancer screening , 2016, Scientific Reports.

[10]  S. Sasaki,et al.  Maternal total caffeine intake, mainly from Japanese and Chinese tea, during pregnancy was associated with risk of preterm birth: the Osaka Maternal and Child Health Study. , 2015, Nutrition research.

[11]  S. McGuire.  World Health Organization. Comprehensive Implementation Plan on Maternal, Infant, and Young Child Nutrition. Geneva, Switzerland, 2014. , 2015, Advances in nutrition.

[12]  Colin Mathers,et al.  Every Newborn: progress, priorities, and potential beyond survival , 2014, The Lancet.

[13]  Keiko Tanaka,et al.  Active and passive maternal smoking during pregnancy and birth outcomes: the Kyushu Okinawa Maternal and Child Health Study , 2013, BMC Pregnancy and Childbirth.

[14]  W. Fawzi,et al.  Mortality risk in preterm and small-for-gestational-age infants in low-income and middle-income countries: a pooled country analysis , 2013, The Lancet.

[15]  Ayesha Sania,et al.  National and regional estimates of term and preterm babies born small for gestational age in 138 low-income and middle-income countries in 2010 , 2013, The Lancet. Global health.

[16]  I. Kawachi,et al.  Income inequality, parental socioeconomic status, and birth outcomes in Japan. , 2013, American journal of epidemiology.

[17]  T. Vrijkotte,et al.  Preterm birth and small for gestational age in relation to alcohol consumption during pregnancy: stronger associations among vulnerable women? results from two large Western-European studies , 2013, BMC Pregnancy and Childbirth.

[18]  Ann-Beth Moller,et al.  National, regional, and worldwide estimates of preterm birth rates in the year 2010 with time trends since 1990 for selected countries: a systematic analysis and implications , 2012, The Lancet.

[19]  Jie Tian,et al.  An Exploratory Spatial Data Analysis of Low Birth Weight Prevalence in Georgia , 2012 .

[20]  H. Takimoto,et al.  The regional difference in children's physical growth between Yaeyama Islands of Okinawa Prefecture and national survey in Japan , 2012 .

[21]  P. Braveman,et al.  Socioeconomic disparities in adverse birth outcomes: a systematic review. , 2010, American journal of preventive medicine.

[22]  Sally Cook,et al.  Geographical variation in infant mortality, stillbirth and low birth weight in Northern Ireland, 1992–2002 , 2010, Journal of Epidemiology & Community Health.

[23]  R. Horgan,et al.  Risk factors for small for gestational age infants. , 2009, Best practice & research. Clinical obstetrics & gynaecology.

[24]  C. Binns,et al.  Trends in the prevalence of low birth weight in Okinawa, Japan: a public health perspective , 2008, Acta paediatrica.

[25]  Andrew B. Lawson,et al.  Bayesian Disease Mapping: Hierarchical Modeling in Spatial Epidemiology , 2008 .

[26]  Y. Doi,et al.  Spatial Clusters of Creutzfeldt-Jakob Disease Mortality in Japan between 1995 and 2004 , 2008, Neuroepidemiology.

[27]  Roberto Romero,et al.  Epidemiology and causes of preterm birth , 2008, The Lancet.

[28]  M. Kaltenbach,et al.  Noise perception, heart rate and blood pressure in relation to aircraft noise in the vicinity of the Frankfurt airport , 2007, Clinical Research in Cardiology.

[29]  P. Gluckman,et al.  Early life events and their consequences for later disease: A life history and evolutionary perspective , 2007, American journal of human biology : the official journal of the Human Biology Council.

[30]  A. Babazono,et al.  [Regional differences in perinatal mortality rates in Japan--an investigation based on vital statistics]. , 2004, Nihon eiseigaku zasshi. Japanese journal of hygiene.

[31]  T. Miyakita,et al.  [Association between the rates of low birth-weight and/or preterm infants and aircraft noise exposure]. , 2003, Nihon eiseigaku zasshi. Japanese journal of hygiene.

[32]  T. Tango,et al.  A test for spatial disease clustering adjusted for multiple testing. , 2000, Statistics in medicine.

[33]  M Kulldorff,et al.  Spatial disease clusters: detection and inference. , 1995, Statistics in medicine.

[34]  R. Marshall.  Mapping disease and mortality rates using empirical Bayes estimators. , 1991, Journal of the Royal Statistical Society. Series C, Applied statistics.

[35]  D. Clayton,et al.  Empirical Bayes estimates of age-standardized relative risks for use in disease mapping. , 1987, Biometrics.

[36]  Martin Charlton,et al.  A Mark 1 Geographical Analysis Machine for the automated analysis of point data sets , 1987, Int. J. Geogr. Inf. Sci..