Links

• Project References
• Preliminary Analysis
• Association Model
• Prediction Model
• Mediation Analysis
• Final Report

Getting Started

Read carefully project outline document to see what you need to do with your project.

Read all support documents to understand what you can do with your project.

Look at the dataset to see what are available (e.g., info/variables)

• BWT --- Birth weight measured in pounds
• GESTWKS --- Gestation measured in weeks
• MAGE --- Mother’s age measured in years
• MNOCIG --- Number of cigarettes the maternal smokes per day
• MHEIGHT --- Mother’s height measured in inches
• MPPWT --- Mother’s pre-pregnancy weight measured in pounds

New variables to consider:

• Maternal BMI & BMI category
• Smoking category

Literature Review

Do an intensive online search to find/gain knowledge about some similar/related topics through some studies. Use the resources of literature search and readings as your scientific support for your project, more particularly for your aims.

Basto-Abreu, A., Barrientos-Gutierrez, T., Zepeda-Tello, R., Camacho, V., de Porras, D., Hernandez-Avila, M. (2017). Obesity. The Relationship of Socioeconomic Status with Body Mass Index Depends on the Socioeconomic Measure Used. Obesity. Volume 26, Issue 1 p. 176-184 https://doi.org/10.1002/oby.22042

Blencowe, H., Krasevec, J., Onis, M. de, Black, R. E., An, X., Stevens, G. A., … Cousens, S. (2019). National, regional, and worldwide estimates of low birthweight in 2015, with trends from 2000: A systematic analysis. The Lancet Global Health, 7(7), e849–e860.

Britto RP, Florêncio TM, Benedito Silva AA, Sesso R, Cavalcante JC, Sawaya AL. Influence of maternal height and weight on low birth weight: a cross-sectional study in poor communities of northeastern Brazil. PLoS One. 2013;8(11):e80159. Published 2013 Nov 11. doi:10.1371/journal.pone.0080159

Cederholm, T., Bosaeus, I., Barazzoni, R., Bauer, J., Van Gossum, A., Klek, S., Muscaritoli, M., Nyulasi, I., Ockenga, J., Schneider, S., De Van Der Scheuren, M., Singer, P. (2015). Diagnostic criteria for malnutrition – An ESPEN Consensus Statement. Clinical Nutrition, Volume 34, Issue 3, Pages 335-340, ISSN 0261-5614, https://doi.org/10.1016/j.clnu.2015.03.001.

Dennis, J., Mollborn, S. (2013) Young maternal age and low birth weight risk: An exploration of racial/ethnic disparities in the birth outcomes of mothers in the United States. The Social Science Journal, Volume 50, Issue 4, 2013, Pages 625-634, ISSN 0362-3319, https://doi.org/10.1016/j.soscij.2013.09.008.

Engle, W. (2006). A Recommendation for the Definition of “Late Preterm” (Near-Term) and the Birth Weight–Gestational Age Classification System. Seminars in Perinatology, Volume 30, Issue 1, Pages 2-7, ISSN 0146-0005

Fraser, A. M., Brockert, J. E., & Ward, R. H. (1995). Association of young maternal age with adverse reproductive outcomes. New England Journal of Medicine, 332(17), 1113–1118.

Gould, J. B., Davey, B., & LeRoy, S. (1989). Socioeconomic differentials and neonatal mortality: Racial comparison of California singletons. Pediatrics, 83(2), 181–186.

Gul R, Iqbal S, Anwar Z, Ahdi SG, Ali SH, Pirzada S. Pre-pregnancy maternal BMI as predictor of neonatal birth weight. PLoS One. 2020;15(10):e0240748. Published 2020 Oct 28. doi:10.1371/journal.pone.0240748

Hack, M., Flannery, D. J., Schluchter, M., Cartar, L., Borawski, E., & Klein, N. (2002). Outcomes in young adulthood for very-low-birth-weight infants. New England Journal of Medicine, 346(3), 149–157.

Hack, M., Gerry Taylor, H., Drotar, D., Schluchter, M., Cartar, L., Andreias, L., et al. (2005). Chronic conditions, functional limitations, and special health care needs of school-aged children born with extremely low-birth-weight in the 1990s. JAMA, 294(3), 318–325.

Husten C. G. (2009). How should we define light or intermittent smoking? Does it matter?. Nicotine & tobacco research : official journal of the Society for Research on Nicotine and Tobacco, 11(2), 111–121. https://doi.org/10.1093/ntr/ntp010

Inoue S, Naruse H, Yorifuji T, et al. Association between Short Maternal Height and Low Birth Weight: a Hospital-based Study in Japan. J Korean Med Sci. 2016;31(3):353-359. doi:10.3346/jkms.2016.31.3.353

Knopik, V. S., Marceau, K., Palmer, R. H., Smith, T. F., & Heath, A. C. (2016). Maternal smoking during pregnancy and offspring birth weight: A genetically-informed approach comparing multiple raters. Behavior Genetics, 46(3), 353–364.

Kowlessar NM, Jiang HJ, Steiner C. Hospital Stays for Newborns, 2011: Statistical Brief #163. 2013 Oct. In: Healthcare Cost and Utilization Project (HCUP) Statistical Briefs [Internet]. Rockville (MD): Agency for Healthcare Research and Quality (US); 2006 Feb-. Available from: https://www.ncbi.nlm.nih.gov/books/NBK173954/

Kuja-Halkola R, D’Onofrio BM, Larsson H, Lichtenstein P (2014) Maternal smoking during pregnancy and adverse outcomes in offspring: genetic and environmental sources of covariance. Behav Genet 44(5):456–467

La Merrill M, Stein CR, Landrigan P, Engel SM, Savitz DA. Prepregnancy body mass index, smoking during pregnancy, and infant birth weight. Ann Epidemiol. 2011;21(6):413-420. doi:10.1016/j.annepidem.2010.11.012

Martin, J. A., Hamilton, B. E., Ventura, S. J., Osterman, M. J., & Mathews, T. J. (2013). Births: Final data for 2011. National vital statistics reports (Vol. 62). Hyattsville, MD: National Center for Health Statistics.

Parascandola M. Commentary: Smoking, birthweight and mortality: Jacob Yerushalmy on self-selection and the pitfalls of causal inference. Int J Epidemiol. 2014;43(5):1373-1377. doi:10.1093/ije/dyu163

Shah Ebrahim, Yerushalmy and the problems of causal inference, International Journal of Epidemiology, Volume 43, Issue 5, October 2014, Pages 1349–1351, https://doi.org/10.1093/ije/dyu186

Srinivas, S., Bastek, J., McShea, M., Foreman, M., Elovitz, M. (2011). 526: Unraveling the racial disparity in preterm birth: experiences of discrimination are associated with preterm birth and low birth weight in black women. American Journal of Obstetrics and Gynecology. Volume 204, Issue 1, Supplement, Pages S209-S210, ISSN 0002-9378, https://doi.org/10.1016/j.ajog.2010.10.545.

Tobacco and Nicotine Cessation During Pregnancy -- Committee opinion by the American College of Obstetricians and Gynecologists (https://www.acog.org/en/Clinical/Clinical%20Guidance/Committee%20Opinion/Articles/2020/05/Tobacco%20and%20Nicotine%20Cessation%20During%20Pregnancy)

VanderWeele TJ. Commentary: Resolutions of the birthweight paradox: competing explanations and analytical insights. Int J Epidemiol. 2014;43(5):1368-1373. doi:10.1093/ije/dyu162

Ventura, S. J., Hamilton, B. E., Mathews, T. J., & Chandra, A. (2003). Trends and variations in smoking during pregnancy and low birth weight: Evidence from the birth certificate, 1990–2000. Pediatrics, 111(Supplement 1), 1176–1180.

Vohr, B., Tyson, J., Wright, L., Perritt, R., Li, L., Poole, W. (2009). Maternal Age, Multiple Birth, and Extremely Low Birth Weight Infants. The Journal of Pediatrics. Volume 154, Issue 4, Pages 498-503.e2, ISSN 0022-3476, https://doi.org/10.1016/j.jpeds.2008.10.044.

Wang, S., Yang, L., Shang, L. et al. Changing trends of birth weight with maternal age: a cross-sectional study in Xi’an city of Northwestern China. BMC Pregnancy Childbirth 20, 744 (2020). https://doi.org/10.1186/s12884-020-03445-2

World Health Organization. (2014). Global Nutrition Targets 2025: Low birth weight policy brief. Retrieved June 20, 2019, from WHO website: http://www.who.int.proxy.lib.pdx.edu/nutrition/publications/globaltargets2025_policybrief_lbw/en/ .

Yerushalmy J. The relationship of parents' cigarette smoking to outcome of pregnancy--implications as to the problem of inferring causation from observed associations. Int J Epidemiol. 2014 Oct;43(5):1355-66. doi: 10.1093/ije/dyu160. PMID: 25301860.

Zhang, W., Yang, TC. Maternal Smoking and Infant Low Birth Weight: Exploring the Biological Mechanism Through the Mother’s Pre-pregnancy Weight Status. Popul Res Policy Rev (2019). https://doi.org/10.1007/s11113-019-09554-x

Project Aims and Hypothesis

Shape your project aims and hypotheses based on reading literature, your knowledge and skills. Make sure your aims tightly connected to the project goal (i.e., association model building, and prediction model building.)

Our research question concerns the 'Birthweight paradox', which refers to the observation made by Yerushalmy (1971) that although the incidence of low birth weight was found to be higher in smoking mother than non-smoking mothers, the mortality rate was actually found to be higher in low birth weight infants with smoking mothers compared to non-smoking mothers. VanderWeele (2014) proposed that there may be unmeasured common causes that contribute to both low birth weight and infant mortality, such that the link between low birth weight and infant mortality is actually explained by this unexplained measure and the smoking status of the mother would not necessarily be a major contributor.

We plan to evaluate the hypothesis that BMI plays a role as a confounder in the relationship between birth-weight and maternal smoking.

Descriptive and Exploratory Analysis

The analysis can be found here: link

Run some descriptive analysis to study distribution of the data for each variable; some exploratory analysis, see the project outline for details.

• For descriptive analysis, you may put all values in a table.
• Then describe each variable based upon the table.
• Study distribution of each variable.
• Use transformation (e.g., gladder function in R) when needed.
• Study scatter plots (in matrix).
• Study correlation among variables (e.g., pairwise correlation matrix).
• Study higher order terms (e.g., from scatter plots, or from indicated literature you have found).
• Check normality assumption for response data.
• Check independence assumption from study design (e.g., in background study section).
• In some cases, you need to make a decision to categorize data. For example, number of cigarettes. How is it distributed? Should it be categorized? How could the categorization be done to address the current interest, e.g., non-smoker/slight smoker/moderate smoker/heavy smoker. Provide references to support the thresholds for the categorization. If there are few observations in a group, how do you make a decision to regroup it to an adjacent group, lower one or upper one?