1. US Linked Birth/Death Data: dataset from the National Vital Statistics System that links birth certificate data to information from death certificates for children under 1 year of age who dies in the US, Puerto Rico, Virgin Islands, and Guam. Linkage is used to provide comprehensive data on age, race/ethnicity of parents, birthweight, gestational period, gravidity, maternal education, maternal smoking, congenital anomalies, etc.
Strong research question: The effect of Planned Parenthood funding cuts to teen pregnancy and adverse birth outcomes, using a DiD analysis. Our hypothesis is thatcuts to Planned Parenthood funding will increase teen pregnancy, preterm deliveries, and low birthweight babies. The US Linked Birth/Infant Death Data Set will be a well-suited dataset to obtain high-quality outcome data, given the expansive information on comprehensive maternal and newborn characteristics. Exposure will be measured as the date of either when the Planned Parenthood bill was instated by President Trump (April 13, 2017) or when individual state governances issued cuts to funding. Effect modification by race/ethnicity and education will be tested. As with all DiD analyses, well thought-out comparison groups need to be identified.
Weak research question: Does maternal smoking increase the risk of congenital anomalies? Data on exposure is well defined in this data set, however, by studying an environmental exposure on an observed outcome that is conditional on whether the child survives to delivery (i.e. live birth) may induce collider bias, if there are other factors that cause fetal death and congenital anomalies. We could try to conduct a quantitative bias analysis; however, other pregnancy cohort datasets that have better follow-up data on the pregnant mother (i.e. data on stillbirths, spontaneous abortions, etc.) may be better suited to answer this question (i.e. data from Kaiser).
2. African Collaborative Center for Microbiome and Genomics Research’s (ACCME) Human Papillomavirus (HPV) and Cervical Cancer Study: Large representative cohort of non-HIV infected women >18 years old living in Abuja, Nigeria, had sexual intercourse, and no previous history of cervical abnormalities, cervical cancer or total abdominal hysterectomy. Enrollment began in February 2014. ACCME collected comprehensive demographic data (adapted questions from NHS study and biological samples including blood, mid-vaginal, ectocervical cell samples, at baseline, 6, 12, 18, and 24 months (but is planning to expand follow-up and participant recruitment).
Strong research question: Does ethnicity (measured by tribe) have an effect on persistent HPV infection among sexually active women in Abuja, Nigeria? Data on tribe, genetic data, SES is collected at baseline and possible confounding by sexual behaviors and incident HPV infection is collected at each follow-up visit
Weak research question: Does anal sex increase the risk of persistent HPV infection? This question would be relatively hard to conduct because for some individuals, reverse causality cannot be ruled out (i.e. all women were eligible regardless of the HPV status). However, if we decide to restrict our analyses to women who were HPV-naïve), we are selecting out a population of women who may have less risky sexual behaviors or may be more likely to clear the infections. Given the relatively little we know about why women are more likely to have persistent HPV vs. clear the infection, we could not draw a complete DAG that would allow us to adjust for covariates to mitigate the selection bias induced (I think).
3. ARIC dataset: prospective cohort study of 16,000 adults aged 45-64 examined twice, 3 years apart in 4 US communities (4,000 participants/community). Primary aim of study is to investigate the aetiology of atherosclerosis and clinical sequelae, and variation in CVD risk factors.
Strong research question: Does glucose-6-phosphate dehydrogenase deficiency protect against coronary heart disease?
Weak research question: Does urban/rural status modify the effect of ethnicity on diabetes? The sites were very specific in their urban and rural status (i.e. Jackson, Mississippi and Minneapolis suburbs, Minnesota were 100% urban), it would be difficult to study effect modification given the study must account for the clustering by site (addition of random effect).