Blencowe H, Cousens S, Jassir FB, Say L, Chou D, Mathers C, et al. National, regional, and worldwide estimates of stillbirth rates in 2015, with trends from 2000: a systematic analysis. Lancet Global Health. 2016;4(2):e98–e108.
Article
PubMed
Google Scholar
Li Z ZR, Hilder L, Sullivan EA. Australia’s mothers and babies 2011. Perinatal statistics series no 28 Cat no PER 59. 2013(Cat. no. PER 50).
Google Scholar
GBD 2016 Mortality Collaborators. Global, regional, and national under-5 mortality, adult mortality, age-specific mortality, and life expectancy, 1970-2016: a systematic analysis for the Global Burden of Disease Study 2016. Lancet (London, England). 2017;390(10100):1084-150.
Flenady V, Wojcieszek AM, Middleton P, Ellwood D, Erwich JJ, Coory M, et al. Stillbirths: recall to action in high-income countries. Lancet (London, England). 2016;387(10019):691-702.
Hilder L, Flenady V, Ellwood D, Donnolley N, Chambers GM. Improving, but could do better: Trends in gestation-specific stillbirth in Australia, 1994-2015. Paediatric and perinatal epidemiology. 2018;32(6):487–94.
Article
PubMed
Google Scholar
United Nations Statistics Division. Standard country or area codes for statistical use (M49) New York: United Nations Department of Economic and Social Affairs 2020 [Available from: https://unstats.un.org/unsd/methodology/m49/.
Choi SKY, Henry A, Hilder L, Gordon A, Jorm L, Chambers GM. Adverse perinatal outcomes in immigrants: a ten-year population-based observational study and assessment of growth charts. Paediatric and perinatal epidemiology. 2019;33(6):421–32.
Article
PubMed
Google Scholar
Mozooni M, Preen DB, Pennell CE. Stillbirth in Western Australia, 2005-2013: the influence of maternal migration and ethnic origin. The Medical journal of Australia. 2018.
de Graaff EC, Wijs LA, Leemaqz S, Dekker GA. Risk factors for stillbirth in a socio-economically disadvantaged urban Australian population. J Maternal-Fetal Neonatal. 2017;30(1):17–22.
Article
Google Scholar
Ibiebele I, Coory M, Smith GC, Boyle FM, Vlack S, Middleton P, et al. Gestational age specific stillbirth risk among Indigenous and non-Indigenous women in Queensland, Australia: a population based study. BMC pregnancy and childbirth. 2016;16(1):159.
Article
PubMed
PubMed Central
Google Scholar
Australian Institute of Health Welfare (AIHW). Stillbirths and neonatal deaths in Australia 2015 and 2016. Canberra: AIHW; 2019.
Google Scholar
Page JM, Thorsten V, Reddy UM, Dudley DJ, Hogue CJR, Saade GR, et al. Potentially preventable stillbirth in a diverse U.S. cohort. Obstetrics and gynecology. 2018;131(2):336–43.
Article
PubMed
PubMed Central
Google Scholar
Queensland Maternal and Perinatal Quality Council. Queensland mothers and babies 2014 and 2015. Brisbane: State of Queensland. p. 2018.
The Consultative Council on Obstetric and Paediatric Mortality and Morbidity. Victoria’s Mothers, Babies, and Children: 2014 and 2015. Melbourne; 2017.
Google Scholar
Australian Institute of Health and Welfare. Perinatal deaths in Australia 2013-2014. Canberra: Australian Government; 2018.
Google Scholar
Flenady V, Koopmans L, Middleton P, Froen JF, Smith GC, Gibbons K, et al. Major risk factors for stillbirth in high-income countries: a systematic review and meta-analysis. Lancet (London, England). 2011;377(9774):1331–40.
Article
Google Scholar
Gordon A, Raynes-Greenow C, McGeechan K, Morris J, Jeffery H. Risk factors for antepartum stillbirth and the influence of maternal age in New South Wales Australia: a population based study. BMC pregnancy and childbirth. 2013;13:12.
Article
PubMed
PubMed Central
Google Scholar
Page JM, Silver RM. Interventions to prevent stillbirth. Seminars in fetal & neonatal medicine. 2017;22(3):135–45.
Article
Google Scholar
Riley RD, Ensor J, Snell KIE, Debray TPA, Altman DG, Moons KGM, et al. External validation of clinical prediction models using big datasets from e-health records or IPD meta-analysis: opportunities and challenges. BMJ (Clinical research ed). 2016;353:i3140.
Google Scholar
Australian Government Bureau of Meteorology (BOM). Climate data online 2018 [Available from: http://www.bom.gov.au/climate/data/.
Perinatal Society of Australia and New Zealand (PSANZ). Perinatal Society of Australia and New Zealand Clinical Practice Guideline for Perinatal Mortality. 2009.
Google Scholar
Riley RD, Ensor J, Snell KIE, Harrell FE, Martin GP, Reitsma JB, et al. Calculating the sample size required for developing a clinical prediction model. BMJ (Clinical research ed). 2020;368:m441.
Google Scholar
@joie_ensor (Joie Ensor). New @Stata package: ‘pmsampsize’ to calculate minimum sample size required for developing a prediction model Based on work by @Richard_D_Riley, @GSCollins, @f2harrell, @Kym_Snell, @CarlMoons, @DanielleBurke88 Type ‘ssc install pmsampsize’. #rstats version coming soon .... Twitter2018.
Australian Institute of Health and Welfare. National Perinatal Data Collection (NPDC) Canberra: Australian Government; 2019 [Available from: https://www.aihw.gov.au/about-our-data/our-data-collections/national-perinatal-data-collection.
Getahun D, Ananth CV, Kinzler WL. Risk factors for antepartum and intrapartum stillbirth: a population-based study. American journal of obstetrics and gynecology. 2007;196(6):499–507.
Article
PubMed
Google Scholar
Ananth CV, Goldenberg RL, Friedman AM, Vintzileos AM. Association of temporal changes in gestational age with perinatal mortality in the United States, 2007-2015. JAMA pediatrics. 2018;172(7):627–34.
Article
PubMed
PubMed Central
Google Scholar
Smith LK, Hindori-Mohangoo AD, Delnord M, Durox M, Szamotulska K, Macfarlane A, et al. Quantifying the burden of stillbirths before 28 weeks of completed gestational age in high-income countries: a population-based study of 19 European countries. Lancet (London, England). 2018.
Perinatal Society of Australia and New Zealand (PSANZ). Clinical practice guideline for care around stillbirth and neonatal death. 2018.
Google Scholar
Andegiorgish AK, Andemariam M, Temesghen S, Ogbai L, Ogbe Z, Zeng L. Neonatal mortality and associated factors in the specialized neonatal care unit Asmara, Eritrea. BMC public health. 2020;20(1):10.
Article
CAS
PubMed
PubMed Central
Google Scholar
Sauerbrei W. The use of resampling methods to simplify regression models in medical statistics. Journal of the Royal Statistical Society: Series C (Applied Statistics). 1999;48(3):313–29.
Google Scholar
Batra P, Higgins C, Chao SM. Previous adverse infant outcomes as predictors of preconception care use: an analysis of the 2010 and 2012 Los Angeles Mommy and Baby (LAMB) Surveys. Maternal and child health journal. 2016;20(6):1170–7.
Article
PubMed
Google Scholar
Kayode GA, Grobbee DE, Amoakoh-Coleman M, Adeleke IT, Ansah E, de Groot JA, et al. Predicting stillbirth in a low resource setting. BMC pregnancy and childbirth. 2016;16:274.
Article
PubMed
PubMed Central
Google Scholar
Sterne JAC, White IR, Carlin JB, Spratt M, Royston P, Kenward MG, et al. Multiple imputation for missing data in epidemiological and clinical research: potential and pitfalls. BMJ (Clinical research ed). 2009;338:b2393.
Article
Google Scholar
Hawthorne G, Elliott P. Imputing cross-sectional missing data: comparison of common techniques. Australian and New Zealand J Psychiatry. 2005;39(7):583–90.
Article
Google Scholar
DeLong ER, DeLong DM, Clarke-Pearson DL. Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach. Biometrics. 1988;44(3):837–45.
Article
CAS
PubMed
Google Scholar
Trudell AS, Tuuli MG, Colditz GA, Macones GA, Odibo AO. A stillbirth calculator: development and internal validation of a clinical prediction model to quantify stillbirth risk. (Research Article). PloS one. 2017;12(3):e0173461.
Article
PubMed
PubMed Central
CAS
Google Scholar
Collins GS, de Groot JA, Dutton S, Omar O, Shanyinde M, Tajar A, et al. External validation of multivariable prediction models: a systematic review of methodological conduct and reporting. BMC medical research methodology. 2014;14:40.
Article
PubMed
PubMed Central
Google Scholar
Flatley C, Gibbons K, Hurst C, Flenady V, Kumar S. Cross-validated prediction model for severe adverse neonatal outcomes in a term, non-anomalous, singleton cohort. BMJ paediatrics open. 2019;3(1):e000424.
Article
PubMed
PubMed Central
Google Scholar
Steyerberg EW, Vickers AJ, Cook NR, Gerds T, Gonen M, Obuchowski N, et al. Assessing the performance of prediction models: a framework for traditional and novel measures. Epidemiology (Cambridge, Mass). 2010;21(1):128–38.
Article
Google Scholar
Ahmed I, Debray TP, Moons KG, Riley RD. Developing and validating risk prediction models in an individual participant data meta-analysis. BMC Med Res Methodology. 2014;14:3.
Article
Google Scholar
Carter JV, Pan J, Rai SN, Galandiuk S. ROC-ing along: evaluation and interpretation of receiver operating characteristic curves. Surgery. 2016;159(6):1638–45.
Article
PubMed
Google Scholar
Steyerberg EW, Eijkemans MJ, Harrell FE Jr, Habbema JD. Prognostic modeling with logistic regression analysis: in search of a sensible strategy in small data sets. Medical Decision Making. 2001;21(1):45–56.
Article
CAS
PubMed
Google Scholar
Vickers AJ, Elkin EB. Decision curve analysis: a novel method for evaluating prediction models. Medical Decision Making. 2006;26(6):565–74.
Article
PubMed
PubMed Central
Google Scholar
Vickers AJ. Prediction models: revolutionary in principle, but do they do more good than harm? J Clin Oncology. 2011;29(22):2951–2.
Article
Google Scholar
Kleinrouweler CE, Cheong-See FM, Collins GS, Kwee A, Thangaratinam S, Khan KS, et al. Prognostic models in obstetrics: available, but far from applicable. Am J Obstetr Gynecol. 2016;214(1):79–90 e36.
Article
Google Scholar
Yerlikaya G, Akolekar R, McPherson K, Syngelaki A, Nicolaides KH. Prediction of stillbirth from maternal demographic and pregnancy characteristics. Ultrasound in obstetrics & gynecology : the official journal of the International Society of Ultrasound in Obstetrics and Gynecology. 2016;48(5):607–12.
Article
CAS
Google Scholar
Christodoulou E, Ma J, Collins GS, Steyerberg EW, Verbakel JY, Van Calster B. A systematic review shows no performance benefit of machine learning over logistic regression for clinical prediction models. Journal of clinical epidemiology. 2019;110:12–22.
Article
PubMed
Google Scholar
Van Calster B, Verbakel JY, Christodoulou E, Steyerberg EW, Collins GS. Statistics versus machine learning: definitions are interesting (but understanding, methodology, and reporting are more important). Journal of clinical epidemiology. 2019;116:137–8.
Article
PubMed
Google Scholar
Steyerberg EW. Clinical prediction models: a practical approach to development, validation, and updating. Second edition.. ed. Cham, Switzerland: Springer; 2019.
Book
Google Scholar
Steyerberg EW, Vergouwe Y. Towards better clinical prediction models: seven steps for development and an ABCD for validation. European heart journal. 2014;35(29):1925–31.
Article
PubMed
PubMed Central
Google Scholar
Goodin A, Delcher C, Valenzuela C, Wang X, Zhu Y, Roussos-Ross D, et al. The power and pitfalls of big data research in obstetrics and gynecology: a consumer’s guide. Obstetrical Gynecological Survey. 2017;72(11):669–82.
Article
PubMed
PubMed Central
Google Scholar
Gordon A, Raynes-Greenow C, McGeechan K, Morris J, Jeffery H. Stillbirth risk in a second pregnancy. Obstetrics and gynecology. 2012;119(3):509–17.
Article
PubMed
Google Scholar
Lamont K, Scott NW, Jones GT, Bhattacharya S. Risk of recurrent stillbirth: systematic review and meta-analysis. BMJ (Clinical research ed). 2015;350:h3080.
Google Scholar
Hernández-Díaz S, Toh S, Cnattingius S. Risk of pre-eclampsia in first and subsequent pregnancies: prospective cohort study. BMJ (Clinical research ed). 2009;338:b2255.
Google Scholar
Vinet E, Chakravarty EF, Simard JF, Clowse M. Use of administrative databases to assess reproductive health issues in rheumatic diseases. Rheumatic diseases clinics of North America. 2018;44(2):327–36.
Article
PubMed
Google Scholar
Ziegler A. Generalized estimating Equations. 1st ed. 2011. New York: Springer New York : Imprint: Springer; 2011.
Book
Google Scholar
TRIPOD Group. Transparent reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD) Checklist: Prediction Model Development and Validation 2019 [Available from: https://www.tripod-statement.org/Downloads.
National Health and Medical Research Council, Australian Research Council, Universities Australia. National statement on ethical conduct in human research. Canberra: Commonwealth of Australia; 2018.
Google Scholar