JØRN OLSEN, May 17, 2016

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1 JØRN OLSEN, May 17, 2016 Research Strategy a. Significance Nitrate (NO 3 - ) is the most commonly found contaminant in the world s aquifers and a major source of contamination of drinking water in the United States (Exner et al. 2014) and Denmark (Schullehner and Hansen 2014). Major environmental inputs of nitrate come from fertilizers, livestock manure, and human sewage (Burrow et al. 2010). Nitrate is extremely water soluble and may partition quickly into groundwater depending on soil conditions. Once the nitrate reaches these aquifers, the aquifers will remain contaminated for decades, even if there is a substantial reduction in the nitrate loading of the surface (World Health Organization (WHO) 2011). Preterm delivery (PTD), low birth weight among full term births (LBW), being small for gestational age (SGA) and birth defects (BD) are important public health issues affecting approximately 11.4%, 8.0%, 10.0% and 3.0% of all births in US in 2014 (Martin et al. 2015, Rynn et al. 2008). Each of these outcomes can lead to reduced quality of life for the affected individual and the health care costs for society are high. PTD has been associated with an increase in infant mortality, asthma, and with impaired learning, motor development, vision and hearing (Glass et al. 2015). Fetal growth restriction (FGR) as indicated by LBW and SGA has been linked with increased risk of infant mortality, cardiovascular disease, diabetes, cancer and other adverse health effects observed later in life (Barker 2006, Intapad et al 2014). BD are the most common cause of infant mortality, and may be associated with substantial morbidity, long term disability and economic and psychological costs to the family (Mathews et al. 2015). Nitrate and nitrite in drinking water have been demonstrated to cause FGR, LBW, BD and other adverse reproductive effects in experimental animals, but only at extremely high levels (>1,000 mg/l) (Fan and Steinberg 1996). Nitrosamines and other N-nitroso compounds, which are formed endogenously after the ingestion of nitrate, have been found to increase CNS birth defects in several animal species (Givelber et al. 1969, Koyama et al. 1970, Pfaffenroth et al. 1974). Nitrates have been implicated as disruptors of gonadal steroidogenesis and thyroid function (Guillette 2006). Subclinical maternal hypothyroidism has been associated with PTD and LBW in humans (Maraka et al. 2016). High levels of nitrate (~ 500 mg/l) in drinking water are a well-recognized cause of methemoglobinemia which is potentially lethal form of anemia commonly referred to as blue baby syndrome in children (Comly 1987). The underlying mechanism is believed to involve an increase in methemoglobin by the reduction of heme iron by nitrites to its ferric state. Fetuses and infants less than 6 months of age are believed to be at particularly high risk of anemia because they have low levels of methemoglobin reductase, which converts methemoglobin back to hemoglobin (Lukens 1998). Maternal anemia has been associated with an increased risk of PTD and LBW (Haider et al. 2013). There are surprisingly few epidemiologic studies on the potential relationship between adverse birth outcomes and contemporary levels of nitrate water contamination given the importance of these outcomes, the ubiquity of contamination of water by nitrate and the strong evidence from experimental studies. Only three epidemiologic studies have examined whether nitrate in drinking water is associated with an increased risk of PTD, LBW or SGA. Burkowski et al. (2001) conducted a population based case control study of 336 cases of PTD and 210 cases of LBW on Prince Edward Island, Canada, which is an area with considerable agriculture and potential for water contamination by nitrates. The study relied on an ecologic exposure assessment of nitrate, which was based on extensive monitoring of both public and private water systems. Significant evidence (p<0.001) of an exposure response relationship was and an increased risk was observed for concentrations above 3.1 mg/l versus <3.1 mg/l for both PTD (Odds Ratio (OR) = 2.01, 95%CI= ), and LBW (OR=2.22,95%CI= ) in this study. Super et al. (1981) in a study in Africa did not observe an increased risk of either PTD or size of the infant at birth in an area of Southwest Africa known to have relatively high levels of nitrates (>20 mg/l). However, this was a very small study (30 cases PTD), which presented results that were not adjusted for any other risk factors and used crude ecologic level exposure data. In the largest study to date, Migeot (2013) reported a significant association between nitrate in drinking water and elevated risk for SGA in a cohort study of 11,446 births (642 cases) in western France, but only in the absence of detectable levels of atrazine metabolites (OR for 3rd versus 1st tertile= 1.74, 95%CI= ). 1

2 A few epidemiologic studies have reported an association between increased risk of BD and exposure to nitrate in drinking water. The earliest report was a case-control study (218 BD cases and controls) in an area of southern Australia where earlier reports had indicated an excess of perinatal mortality (Dorsch et al. 1984). The risk of BD was found to increase with concentration of nitrate in drinking water, a significantly increase in risk of BD (OR=4.1, 95%CI= ) was observed among children of women who drank water containing greater than 15 mg/l of nitrate. The association was particularly strong in this study for CNS defects. Arbuckle et al. (1988) reported a non-significant increase in the risk of CNS defects (OR=2.30,95%CI= ) in a case-control study (130 cases, 260 controls) in New Brunswick, Canada with nitrate exposures in excess of 16 mg/l but only when the source of the water was from private wells. A significant increased risk (OR=4.0, 95%CI= ) of anencephaly was reported among women who consumed water containing > 45 mg/l in a case-control study (538 cases and controls) in California (Croen et al. 2001). Most recently, Brender et al. (2013) reported the findings from a large (3,300 BD cases and 1,121 controls) case-control study in Texas and Iowa, which was a part of the National Birth Defects Prevention Study. This is the only study to date that has accounted for water consumption patterns, diet the use of nitrosatable drugs and had complete residential histories during the pregnancies. Mothers of babies with spina bifida were twice as likely (OR=2.0, 95%CI=1.3,3.2) to consume > 5 mg nitrate per day from water. A significant increased risk of limb deficiency, cleft palate and cleft lip was also observed among the babies of mothers who consumed high levels of nitrate in drinking water in this study. Once ingested nitrate can be converted to nitrite which along with nitrite from water, diet and endogenous sources can react with nitrosatable compounds such as amine or amide containing drugs to form nitrosamines and other nitroso compounds, which are teratogens in animal models (Nagao et al and Platzek et al. 1983). An association has been observed in the National Birth Defects Prevention Study between the use of nitrosatable drugs and the risk of PTD (Vuong et al. 2016) and neural tube BD (Brender et al and 2013) particularly in conjunction with higher levels of dietary nitrite. With the exception of the National Birth Defects Prevention studies (Brender et, 2013), the epidemiologic studies conducted to date suffer from a number of serious limitations. One common limitation of most prior studies are their uncertain assessments of nitrate exposure due to difficulties in estimation of individual exposures, mapping water systems and limited monitoring of nitrate in public and private water systems. Many of the studies also suffer from small sample size and limited information on other risk factors and dietary sources of nitrate and nitrite exposure. We seek to fill this major gap in the literature with our proposed studies which would include by far the largest cohort ever studied, with well characterized individual level estimates of nitrate in drinking water, information on other important risk factors, dietary intake of nitrate and nitrite and the use of nitrosatable drugs. b. Innovation. Our study has a number of advantages over prior studies, which make it highly innovative and a major step forward in addressing these questions. These advantages stem from several unique opportunities for research in Denmark, which are largely derived from the our ability to characterize individual level exposure to nitrate in drinking water, and the capability in Denmark to link individual level health, environmental and other record systems for research purposes. Due to these capabilities we will be able to: Conduct studies in which the entire population of Denmark is treated as a cohort resulting in a study size (~1 million births), which will make our study the most statistically powerful study ever conducted. Most of the prior studies described above were based on just a few hundred births. The largest of the studies on PTD and LBW was the study by Migeot (2013), which only included about 11,000 births. The case-control study of BD by Brender (2013) was based on 3,300 cases while we estimate that our study will include about 19,000 cases of BD. Conduct supplementary analyses with data from the Danish National Birth Cohort (DNBC, now called Better Health in Generations) (Olsen et al. 2001) in which we will have the ability to account for personal characteristics such as water consumption patterns, intake of nitrate and nitrite from dietary sources, prepregnancy maternal weight, smoking, alcohol, parity, occupation, income and education. None of the prior studies considered these potentially important determinants of nitrate exposure with the exception of the study by Brender et al. (2013). Furthermore by linking the DNBC with prescription records we will also be 2

3 able to examine possible effect modification of nitrate due to the use of nitrosatable drugs, which has only been examined for BD in the study by Brender et al. (2013). Our study would be the first to examine this potential interaction for the other outcomes. Quantify levels of nitrate in drinking water for individuals in the study by linking complete residential addresses with private and public water systems and estimates of nitrate in drinking water in Denmark. Many of the prior studies (e.g. Super et al and Dorsch et al. 1984) relied on crude ecologic data to identify areas with high or low levels of nitrate, and did not estimate individual levels of water contamination. There are also several aspects of water quality Denmark that make this an ideal location for this study including: Tap water in Denmark is not chlorinated and relatively free of other contaminants thus reducing the potential of confounding by other water contaminants. In Denmark all drinking water is derived from simple-treated groundwater, which is very clean and bottle water quality at the tap (Thomsen et al. 2014). Furthermore, pesticides in drinking water are generally non-detectable or well below applicable standards in Denmark (Thorling et al. 2015). Danes rarely drink bottled water because their tap water quality is so high. Only 0.03% of the water supply in Denmark is from bottled water ( Use of bottle water may be an important source of exposure misclassification in prior studies conducted elsewhere. For example, approximately 30% of the participants in the study by Brender et al. (2013) exclusively used bottled water, and estimation of nitrate levels in bottled water was very problematic. Danes also do not generally filter their water. There is a wide range of nitrate levels in drinking water in Denmark with some areas experiencing relatively high levels. Denmark is an intensive agricultural country, with a relative high input of nitrogen fertilizers. Almost all drinking water comes from groundwater, and large parts of the aquifers are vulnerable to nitrate leaching due to specific hydrogeological and geochemical conditions. Denmark has been reported to have the largest percentage of the population exposed to elevated nitrate concentrations (>25 mg/l) in the European Union (van Grinsven et al. 2010). However, nearly 100% of the population receiving public water, had exposure concentrations below the U.S. Environmental Protection Agency s (EPA) Maximum Concentration Limit (MCL) of 10 mg/l (Schullehner and Hansen 2014) making it a useful setting for evaluating the potential adverse health effects associated with exposures below the current MCL. Denmark is a relatively economically, racially and culturally homogenous population in which all individuals have free access to high quality prenatal health care. This makes it far less likely that there will be appreciable confounding by socioeconomic factors as in the U.S. where there are substantial inequalities in income and access to health care. Finally, our study will be unique in being the first to examine the association between water contamination by nitrate and the risk of VPTD, reduced birth head circumference (BHC) and birth length (BL). It will also be the second study to ever examine a possible synergism between the use of nitrosatable drugs and nitrates in drinking water in the causation of CNS BD. c. Approach c.1. Study Populations Population Based Cohort (PBC): Data at the individual level from different registries in Denmark can be linked using a unique personal identifier number (CPR), making it possible to study the entire population in Denmark as a cohort (Schmidt et al. 2014). The PBC will include all live born singleton births in Denmark from 1997 to 2015, which will be identified from the Danish National Patient Register (NPR) (Lynge 2011) resulting in a study population of approximately 1,152,175 births (Statistics Denmark 2015). We restricted our study to this time period to ensure the highest quality available nitrate exposure data and because information on maternal tobacco smoking during pregnancy is available from the medical birth registry starting in 1997 (Knudsen and Olsen, 1998). The NPR also includes data on gestational age, birth weight, birth head circumference, birth length, birth defects, date of birth, sex, parity, mode of delivery, presence of labor, 3

4 maternal ethnicity, and maternal age. Parental education, household income and population density of the municipality of the home addresses during pregnancy will be obtained from Statistic Denmark. The PBC study will have very strong statistical power due to its size and have no bias related to selection or loss of follow-up. However, it will lack information on a few possibly important covariates including: dietary intake of nitrate and nitrite, drinking water use habits, alcohol use and pre-pregnancy body mass index (BMI) (kg/m 2 ) and use of folate supplements. In order to overcome this limitation, complimentary analyses will be conducted with using the Danish National Birth Cohort (DNBC). Danish National Birth Cohort (DNBC): The DNBC recruited 101,042 pregnant women during 1996 to 2000 (Olsen et al and 2007). In addition to the information available from the NPR, the DNBC has detailed data on maternal drinking water use habits, alcohol use, pre-pregnancy BMI, folate supplementation and dietary information from a self-administered semi-quantitative food frequency questionnaire (FFQ) that was mailed at mid-pregnancy (Olsen et al. 2007) as well as other potential risk factors such as prenatal parental active and passive smoking and maternal drug use. Our study will be restricted to live singleton births and to children of mothers who completed the FFQ resulting in a total sample size of 66,933 children. c.2. Outcomes: The adverse birth outcomes that will be examined in this study will all be identified from the NPR registry and defined as follows: PTD cases will be defined as infants born before 37 completed weeks of gestation and VPTD cases as infants born before 32 completed weeks of gestation. Gestational age is estimated as the difference between last menstrual period and the date of delivery in most cases and from ultrasound scans for the more recent data. LBW cases will be defined as infants with a weight at birth of less than 2,500 grams who were born at term (i.e. after 37 completed weeks of gestation). SGA cases will be defined as infants of birth weight that is below the 10 th percentile of infants within the same gestational week, the same sex and born within the same year in the PBC, and DNBC cohorts. An alternative analysis will be conducted using the criteria of Marsal et al (2002) for SGA having a birth weight >2 standard deviations below the mean for a given gestational age on the basis of fetal weights derived from serial ultrasounds in a Scandinavian population. BCH and BL will be analyzed as outcomes on a continuous scale. BD will be inpatient and outpatient diagnoses, which are contained in the NPR registry. Particular attention will be given to analyses of nervous system (ICD 10:Q00-Q07), neural tube birth (ICD10: Q00, Q05 and Q07) and cleft palate (ICD10 Q35) birth defects based on prior studies reviewed above (Dorsch et al. 1984, Croen 2011, Brender 2013). c.3. Assessment of Nitrate in Drinking Water: Extensive data from certified labs on nitrate concentrations in public and private water systems are available for the past 35 years from a national database called Jupiter ( All of the public water systems in Denmark have reported results for nitrate measured as total Nitrogen since the 1990 s (the start of our study is 1997). An approach has been developed for utilizing this data to estimate nitrate water concentrations for all the individual home addresses in Denmark for epidemiologic purposes (Schullehner and Hansen 2014, Schullehner et al. 2016). For public water, the boundaries of 2852 individual water supply areas, which cover the entire Denmark, have been mapped over time and, linked with the nitrate exposure data from Jupiter and with individual addresses within these water systems. Complete residential address histories will be obtained for the both cohorts from the Civil Registration System (Pedersen 2011). All addresses in Denmark have been geocoded since 1978 (Pedersen 2011). Data is also available from Jupiter on nitrate water concentrations in private wells, although the data is far more limited than for public water systems (see discussion in preliminary data). Trimester-specific and full pregnancy weighted mean exposure will be estimated. For BD we will also estimate exposures for the three months prior to conception, since its possible that there could be a germline effect. An average weighted by the time lived at each address will be estimated for women who resided at more than one address during their pregnancy. 4

5 c.3. Assessment of Nitrate in Diet: Dietary intake is also a very important source of nitrates and nitrites. Dietary intake of nitrate has been associated with an increased risk of neural tube defects in two studies (Brender 2004, Croen et al. 2001). In general, diet will be the main source of nitrate intake when nitrate levels in drinking water are low (< 10 mg/l) (WHO 2011). The self-administered maternal FFQ, which was used in DNBC was specifically designed to address dietary habits of pregnant women in Denmark over the past month and during entire pregnancy (Olsen et al. 2007). The FFQ included 360 food and drink items and specific questions on supplements and vitamins (Olsen et al. 2007). Individual dietary intakes of nitrates, nitrites and nitrosamines will be estimated adapting a methodology that was developed as part of the U.S. National Birth Defects Prevention Study (Griesenbeck et al. 2009) and as part of the NewGeneris study (Merlo et al. 2009). Estimated intakes will be calculated for each relevant food and drink item using national and European food monitoring data and standardized recipes already used within the DNBC for mixed dishes (Merlo et al. 2009, Pedersen et al. 2009). These estimated means will be multiplied by standard serving sizes assigned to each food and drink, and the amount of nitrate, nitrite and nitrosamines across all items will be summed to give estimates of total dietary intake of nitrate, nitrite and nitrosamines, respectively. Individual intakes with and without water consumption will be computed since it is debatable whether simultaneously co-intake of micronutrients can modify the adverse effects of nitrate, nitrite and nitrosamines. c.4. Assessment of Nitrosatable Drugs: Information on prescription drug use of nitrosatable drugs will be obtained from the Danish National Prescription Registry (DNPC), which has individual level information on dispensed prescriptions for the entire Danish population since 1994 (Kildemoes et al. 2011). The participants of both the PBC and the DNBC cohorts will be linked to records in the DNPC by using their CPR number. In addition, the DNBC questionnaires that were administered in the 12 th and 30 th week of pregnancy included questions on medicine usage. The DNBC questionnaire information will be particularly useful for identifying non-prescription drug use (e.g. pseudoephedrine), which will not be captured by the DNPC. Nitrosatable drugs will be identified by cross-referencing with lists developed for previous studies (Brender et al. 2011, McKean-Cowdin et al., 2003 and Brambilla and Martelli, 2007) and a review of the literature. The drugs will be further categorized based on the presence of amine (primary, secondary, tertiary) or amide groups. Primary amines do not form stable N-nitroso compounds and will be excluded. d. Statistical Analysis: Exploratory analyses will first be conducted in order to develop an understanding of the distribution and correlations between the covariates, identify extreme values and missing data for the key study outcomes and covariates. We will use multiple imputation (MI) methods (Rubin 1987), in which information on observed covariates is used to impute the value of the missing outcome using regression techniques, if covariates that are included in the analysis result in a loss of more than 10% of the data due to missing values. MI is a useful alternative to avoid the potential biases of restricting the analyses to those with complete data. Crude and multivariable regression analyses will be conducted to examine the association between the study outcomes and nitrate exposure variables. The regression methods will vary for the different aims of the study. For all analyses, covariates that will be considered for inclusion in the analyses for the PBC include maternal ethnicity and age, income, education, maternal smoking, population density of the municipality at birth (as a proxy of urbanicity), parity, sex and season of birth. In addition, for the LBW, BCH and BL analyses gestational age will be considered as a covariate. In the DNBC studies, we will also consider including in the models the more detailed information on both active and passive smoking, pre-pregnancy weight, height, maternal alcohol consumption, intake of folate supplements and dietary intake of nitrate and nitrite. We will consider adjustments for the consumption of bottled water in the analyses of the DNBC cohort. Covariates that are found to change the effect of the nitrate exposure variables by more than 10% or that significantly improve the fit will be included in the models. Statistical interactions between the covariates and nitrate exposures will be examined in the analysis. We also examine the possible interaction between the use of nitrate and nitrosatable drugs and other covariates in the analysis. The statistical significance of the 5

6 interactions will be tested using a likelihood ratio test. Models will be fitted using continuous and categorical (quintiles) measures of nitrate exposure. The assumption of linearity of the exposure-response relationship will be evaluated by fitting spline models (Govindarajulu et al. 2009). Analyses will be conducted using the estimated concentration of nitrate in drinking water (mg/l) for each individual, which will be averaged over the addresses lived in during their entire pregnancy and during each trimester. In addition, for the DNBC we will model a measure of the average dose (mg/day) that takes into account the amount of water consumed taking into account the use of bottled water and other drinks. Finally, we will fit models using an estimate of the average total dose (mg/day), which takes into account both dietary intake and water consumption. We will use general estimating equation (GEE) models with robust standard errors (Liang and Zeger 1986) in order to account for the non-independence of observations from mothers who gave birth more than once in the cohorts. The goodness of fit of all models will be evaluated to determine whether there is model misspecification. Specific details for modeling outcomes for each of the aims of the study are described below. Aim 1: To assess whether maternal exposures to nitrate in drinking water during pregnancy is associated with an increased risk of premature delivery of the child. Cox proportional hazard models will be used to estimate the hazard ratios (HR) and 95% confidence intervals (CI) for PTD and VPTD for both the PBC and the DNBC analyses. Gestational age will be used as the underlying time variable. Women will be censored at 37 weeks for PTD and at 32 weeks for VPTD. Average and trimester specific nitrate water concentration levels will be treated as time dependent variables by estimating these concentrations at the gestational age of the cases for all members of their risk set. Aim 2: To assess whether maternal exposures to nitrate in drinking water during pregnancy is associated with an increased risk of fetal growth restriction. Log-linear binomial regression models will be used to estimate risk ratios (RR) and their 95% CI for LBW and SGA using the DNBC. The mean change per unit of nitrate exposure and the corresponding 95%CI in mean BHC and BL will be estimated using ordinary linear regression models. Analyses of residuals for normality will be examined using a probability normal density plot and formally assessed using the Shapiro- Wilk test for normality. Log transformations of the BHC and BD outcome variables will be considered if there is evidence of non-normality of the residuals. Aim 3. To assess whether maternal exposures to nitrate in drinking water during pregnancy is associated with an increased risk of birth defects. Log linear binomial regression models will be used to analyze the data for the DNBC. Separate models will be fitted for all BD, specific nervous system defects, neural tube defects and cleft lip or palate. Sensitivity Analyses will be conducted to assess the impact of potential exposure misclassification on the findings from our study. A six-category index has been developed of the quality of the nitrate water concentrations for all households in Denmark over time (Schullehner et al. 2016). This index was based on the number of samples available for a particular time period and water system, and the variability of the exposure estimates. We will perform sensitivity analyses in which we restrict the data to varying levels of data quality. A sensitivity analysis will also be performed in which we eliminate households that are supplied by private well water, since the uncertainty for these households is greater than that for those served by public water systems. Statistical Power Estimates for the statistical power of the analyses of the cohorts are presented in Table 1. These estimates are for varying levels of the relative risk comparing individuals with > 10 mg/l (US EPA MCL) with those < 10 mg/l of nitrate in their drinking water. We assumed that approximately 10% of the Danish population receives drinking water with > 10 mg/l of nitrate based on the study by Schullehner and Hansen (2014). 6

7 The PBC study appears to be extremely powerful for most of the outcomes with a power of nearly 100% for detecting a relative risk of 1.1 for all of the outcomes except for the specific birth defects (i.e. nervous system, neural tube and cleft palate/lip defects). However, the power of the study will be strong (>0.9) for a relative risk of 2.0 or greater for these birth defects much substantially smaller than the PBC study thus this study should have adequate power to detect an excess risk if one exists (Brender et al. 2013, Croen et al. 2001). The power of the DNBC is obviously lower than that of the PBC, but still excellent for PTD, LBW and SGA (>0.90 for RR=1.20). The power of the DNBC for BD is poor, but it still will be useful to compare the findings from this analysis to that from the PBC, particularly if a large increase in risk is observed in the PBC. The power of the PBC and the DNBC to detect a change in the mean level of BHC and BL is excellent. The PBC study will have adequate power (p=0.86) to detect a change in the mean head circumference of 0.01 centimeters (cm), and the DNBC will have adequate power (p=0.84) to detect a change of 0.04 cm assuming an average circumference of 35.1 cm (Matthiesen et al. 2016), alpha=0.05 and a 10% exposure frequency. Similarly, the PBC will have adequate power (p=0.87) to detect a change in body length of as small as 0.01 cm, and the DNBC will have adequate power (p=0.84) to detect a change in body length of 0.04 cm assuming an average of 51.8 cm (Rom et al. 2014), alpha=0.05 and a 10% exposure frequency. Table 1: Statistical power to detect varying relative risks (RR) Population Based Cohort (N=1,152,175) Danish National Birth Cohort (N=66,933) Power to Detect RR of Power to Detect RR of Outcome Cases Cases Preterm Delivery 70, , Very Preterm Delivery 21, , Low Birth Weight 50, Small Gestational Age 60, All Birth Defects 19, Nervous System Neural Tube Cleft Palate or Lip 1, Assumptions: a. p < 0.05 (one sided)b. 10% of the population is exposed to levels > 10 mg/l c. Approximately 7%, 2.1%, 5%, 6%, and 2.11% are PTD, VPTD, LBW, SGA, and BD respectively based on rates in Denmark obtained from several sources. e. Timeline: Months (M) 1-6: Notify the Danish Data Protection Agency. Obtain approval from the University of Illinois at Chicago Human Subject Review Board. Hire a data manager, post doctoral fellow and research assistant. M 7-9: Apply for becoming a user on Denmark Statistics. Obtain and clean datasets for analyses and create record linkages. M 10-12: Create individual estimates of nitrate exposure from water and dietary sources, and for nitrosatable drugs. M 13-18: Perform analyses for Aim 1 M 19-24: Write report for findings from Aim 1. Communicate the results with collaborators, stakeholders and at international scientific conferences. M 25-27: Perform statistical analyses for Aim 2 M 28-33: Write report for findings from Aim 2. Communicate the results with collaborators, stakeholders and at international scientific conferences M 34-36: Perform statistical analyses for Aim 3 Communicate the results with collaborators, stakeholders and at international scientific conferences. M 37-42: Write report for findings from Aim 3. M 43-48: Finalize publication of findings from all 3 Aims 7

8 f. Potential Challenges: We do not foresee any major challenges to the conduct of our proposed research. Estimates for nitrates in drinking water for residences in Denmark have already been developed. Linkage of the nitrate estimates with health data and social registers is now straightforward using information on residence in the Danish Civil Registration System (Pedersen et al. 2011). Numerous studies have already been performed in Denmark using a similar approach to ours. Several members of our research team (Jørn Olsen, Marie Pedersen, Torben Sigsgaard) have extensive experience in successfully working with these databases. Dr. Olsen who is a co-investigator on this study is the Principle Investigator of the DNBC and he has assured us that we will have access to the DNBC data (see letter of support from Dr. Olsen). As with all epidemiologic studies, there are limitations in the PBC and DNBC studies that might threaten the validity of our findings. However, these limitations are different for the PBC and DNBC studies, and in many ways these analyses compliment one another in terms of overcoming the limitations. The analyses based on the PBC data will lack data on some important risk factors (e.g. maternal BMI, folate supplements and alcohol use), information on dietary sources of nitrates and nitrites, and water use habits. It is unclear whether any of these variables are associated with nitrate in drinking water and or whether they would confound any observed associations in the PBC analyses. However, we will be able to check this by conducting parallel analyses using the DNBC data, which does have information on these variables. The main limitation of the DNBC analyses is that it has low statistical power for studying specific BD. On the other hand, the PBC analyses will have more than adequate power for studying all of the adverse birth outcomes. Furthermore, its important to note that the DNBC sample size is in fact greater than all, but one of the prior studies that demonstrated an increased risk of neural tube defects. If the findings in these studies are causal we should be able to detect them in our study. Another possible concern with the DNBC data is that only about 50% of the women invited participated in the study (Olsen et al. 2001), and thus there could be a potential for selection bias. This seems very unlikely given that women were enrolled in their first trimester and would have no knowledge of their birth outcomes, it also seems unlikely that exposure to nitrate in drinking water would differ among those who did and those who did not enroll in the study. Selection bias is not an issue for the PBC analyses, which will include the entire population of children born Denmark between 1997 and One limitation that both analyses have in common is the uncertainty in our estimates of exposure to nitrate particularly from drinking private well water. Private wells in Denmark are not routinely monitored, and thus the database for developing estimates of exposure for private wells was far more limited than for public water systems (Schullehner and Hansen 2014). This is a common issue affecting all prior epidemiologic studies, since private wells are also not routinely monitored in the U.S. or other countries. However, this is not a major challenge for our study given that only 3% of the Danish population gets their water from private wells. We will also evaluate the impact of the potential exposure misclassification by conducting sensitivity analyses in which we drop births from households with private wells. Furthermore, we will be able to restrict our analysis to individuals from households where we do confidence that the estimates of nitrate are high quality. Despite these challenges, we strongly believe that the strengths of our study far outweigh the weaknesses and that our study would be the most informative epidemiologic study that has ever been conducted concerning the potential impact of nitrate in drinking water on the risk of adverse birth outcomes. g. Preliminary Data As previously described, an approach to estimating nitrate exposure in drinking water in Denmark has already been published (Schullehner and Hansen 2014). Public water supply areas (n=2,852) in Denmark have been mapped (Fig. 1) and linked with nitrate quality measurements from the public water systems that supply them. The number of nitrate measurements reached at least one per year for nearly all public water systems by 1990 (Fig. 2). Our study will begin in 1997 and thus our study will have nearly complete data for all public water systems. However, the number of measurements for private wells is much more sparse, although it has been increasing over time (Fig. 2). 8

9 Fig. 1. Ten georegions of Denmark (I-X) and 2,852 public water supply areas. Fig. 2. Number of public water supplies and private wells with at least one nitrate measurement per year. This methodology for estimating nitrate level at a household level (Schullehner and Hansen 2014) has just recently been adapted for estimating nitrate water concentrations at the household level for use in an epidemiologic investigation (Schullehner et al., In Preparation). Waterworks were first divided into public supplies that were assigned to the water supply areas, which they supply, and private wells that were handled as point data. All Danish addresses were classified as either being supplied by a public supply or by a private well and annual nitrate concentrations from 1978 to 2013 were assigned to the addresses using spatial methods. For years with no samples, concentrations were estimated by interpolation and/or back and forward filling. A database was obtained which contained the addresses of all Danish household addresses and the exact date on which individuals moved to and from the address from 1968 to From 1978 it was possible to geocode approximately 98% of the households by matches considered of the highest quality. Estimates of nitrate in drinking water for every person living in Denmark between 1968 and 2013 could this be achieved by linkage of individual address histories with the annual estimates of nitrate in drinking water at each address. In Fig. 2, the distribution of household nitrate concentrations is shown for public and private wells. One can see a decrease over nitrate concentrations over time in public but not in private water systems. This is because there was frequently just one data point for the private wells, which was used for multiple years. For the time period of our study ( ) approximately 10% of the households receiving public water and over 50% of the public water systems had estimated concentrations that exceeded the EPA MCL of 10 mg/l. A parameter was been developed to account for uncertainty of the estimates of drinking water nitrate at a given household in order to permit sensitivity analyses. The individual household estimates were classified into 6 quality categories based on the number of samples available and the range of concentrations in a given year. The distribution of the exposure data by the uncertainty categories and calendar year is presented in Fig. 3. 9

10 Fig. 3. Classification of all publically and privately supplied households by year. Over 95% of the data for household receiving public water during the time period of our study ( ) is in the top two highest quality groups (0 and 1). The data quality for private systems is considerably poorer with approximately 60 to 75% of the data in the two lowest quality groups (4 and 5) during the time period of our study. Although the quality of the data is low for the well data these data are still suitable for epidemiologic study since the analyses may be restricted to only include high quality data and sensitivity analyses can be performed to determine the effect of such restrictions. Fig. 4. Uncertainty parameter by publicly (a) and privately (b) supplied households. Finally, we have recently submitted for publication the findings from an ecologic study of the effects of water contamination by nitrates and atrazine on LBW, VLBW, PTD and VPTD which included a total of 134,258 singletons births born between January 1, 2004 and December 31, 2008 from 46 counties in four Midwestern states (Indiana, Iowa, Missouri and Ohio) with public water systems that were included in the U.S. EPA s atrazine monitoring program (Stayner et al. submitted May 2016). Multivariable negative binomial models fitted with generalized estimating equations were used to examine the association between the exposures and adverse birth outcomes. Models were fitted with varying restrictions on the percentage of private well usage in the counties in order to limit the degree of exposure misclassification. Exposure to nitrates was associated with a highly significant increased rate of VPTD (RR 1ppb =1.08, 95%CI=1.02,1.15;p=0.007) and VLBW (RR 1ppm =1.17, 95%CI=1.08,1.25;p= ) when well use was restricted to 20% and the exposure was averaged over 9 months prior to birth. The exposure-response was found to be linear on a log scale based on fitting a restricted cubic spline model as shown in Figure 5. It is also noteworthy, that the results were 10

11 unchanged when the data was restricted to concentrations below the current EPA MCL. The findings from this study need to be interpreted cautiously given its ecologic design, and limitations in the data for the exposures and other risk factors. Nonetheless, these findings raise serious concerns about the potential adverse effects of this common water contaminant, and provide strong support of the need for additional studies with individual level outcome data and estimates of exposure and data on other risk factors, dietary sources of nitrate and nitrosatable drug use. Our proposal is specifically designed to meet this need. Figure 5: Results from fitting restricted cubic spline (3 knots) model for nitrates exposure (ppm) averaged over 9 months prior to birth with a restriction of < 20% well usage. Models controlled for child s sex, race and ethnicity, calendar year, season of birth, maternal education, median income, population density, and smoking among women. 11