Volume 13 Supplement 1
The All Our Babies pregnancy cohort: design, methods, and participant characteristics
© McDonald et al; licensee BioMed Central Ltd. 2013
Published: 31 January 2013
The prospective cohort study design is ideal for examining diseases of public health importance, as its inherent temporal nature renders it advantageous for studying early life influences on health outcomes and research questions of aetiological significance. This paper will describe the development and characteristics of the All Our Babies (AOB) study, a prospective pregnancy cohort in Calgary, Alberta, Canada designed to examine determinants of maternal, infant, and child outcomes and identify barriers and facilitators in health care utilization.
Women were recruited from health care offices, communities, and through Calgary Laboratory Services before 25 weeks gestation from May 2008 to December 2010. Participants completed two questionnaires during pregnancy, a third at 4 months postpartum, and are currently being followed-up with questionnaires at 12, 24, and 36 months. Data was collected on pregnancy history, demographics, lifestyle, health care utilization, physical and mental health, parenting, and child developmental outcomes and milestones. In addition, biological/serological and genetic markers can be extracted from collected maternal and cord blood samples.
A total of 4011 pregnant women were eligible for recruitment into the AOB study. Of this, 3388 women completed at least one survey. The majority of participants were less than 35 years of age, Caucasian, Canadian born, married or in a common-law relationship, well-educated, and reported household incomes above the Calgary median. Women who discontinued after the first survey (n=123) were typically younger, non-Caucasian, foreign-born, had lower education and household income levels, were less likely to be married or in a common-law relationship, and had poor psychosocial health in early pregnancy. In general, AOB participants reflect the pregnant and parenting population at local and provincial levels, and perinatal indicators from the study are comparable to perinatal surveillance data.
The extensive and rich data collected in the AOB cohort provides the opportunity to answer complex questions about the relationships between biology, early experiences, and developmental outcomes. This cohort will contribute to the understanding of the biologic mechanisms and social/environmental pathways underlying associations between early and later life outcomes, gene-environment interactions, and developmental trajectories among children.
Population-based cohort studies are important sources of data to investigate life course processes and to identify aetiological determinants of health and disease outcomes in later life . As they are not specific to a diseased population, they provide insight on what constitutes typical trajectories and minor variations within the normal range of development. Pregnancy and birth cohort studies are particularly salient for studying early origins of health and disease that begin in fetal life and infancy. Indeed, the causal underpinnings of many common diseases in adulthood (e.g., cardiovascular disease, obesity, psychopathology) have roots in utero and the early postnatal phase [2–8]. Early identification of threats to well-being is important for the development of preventive and early intervention strategies to optimize health and health care for individuals and communities. Cohort studies can provide important aetiological, descriptive and surveillance information about early risk factors for disease that can inform research, policy, programs, and practice.
Advantages of cohort studies for examining development and links between early and later life outcomes are well established [9–11]. The prospective cohort study design is especially suited for examining associations that require consideration of temporality and are less subject to recall bias and reverse-causality bias compared to other epidemiological study designs [1, 9]. An important strength of longitudinal studies is their potential for investigating trajectories of development and identifying sensitive periods of risk or resilience [9, 12]. Furthermore, in longitudinal research, there is a higher probability of discovering true exposure outcome relationships (i.e., causal relationships) when one exists . An additional advantage relates to efficiency gained through the breadth of data collection and ability to assess a range of possible causes and outcome variables, although in cases of rare but important outcomes, collaboration with similar studies, or a more suitable design (i.e., case-control) is warranted .
The prospective cohort study has emerged as an important study design to investigate gene-environment interactions in diseases of major public health importance . Although the case-control study remains a widely used method for examining genetic and environmental determinants of complex disease, they are subject to significant sources of bias that relate to subject selection and measurement of exposures and outcomes . Prospective cohort studies and their substudies (e.g., nested case-control studies) can address some of these irremediable sources of bias and offer complementary and innovative sources of information for studying early origins of later disease and gene-environment interactions. A number of prospective pregnancy and birth cohorts studies exist in both developing and developed countries, and many have contributed to understanding the role of the pre- and postnatal environment on later life health, crucial for aetiological and prevention research; examples include European cohorts such as The Avon Longitudinal Study of Parents and Children (ALSPAC) , the Generation-R study , the Danish National Birth Cohort study , the Millennium Cohort Study , and North American cohorts such as the National Children’s Study , and the Ottawa and Kingston Birth Cohort . This paper will describe the development and characteristics of the All Our Babies (AOB) study, a prospective pregnancy cohort study in Calgary, Alberta, Canada.
The AOB study (n=3388) was designed to examine maternal and infant outcomes during the perinatal period and to identify current barriers and facilitators to accessing health care services in Calgary, Alberta. A further objective that was incorporated approximately one year after the start of recruitment was to examine biological and environmental determinants of adverse birth outcomes, specifically spontaneous preterm birth, for which approximately half of the AOB sample (n=1862) provided blood samples at two time points during pregnancy, and cord blood, when retrievable, was collected at birth (n=1399). The biological data collection and storage provides whole blood, plasma, and serum samples from which lymphocytes, cytokines, and proteins may be isolated and RNA and DNA will be extracted for micro-array analysis and future measurement. Cord blood samples will be used for future studies. Biological data collection methodology has been previously described . Currently, the AOB study is collecting observational data beyond the perinatal period at 12 months, 24 months, and 36 months. Future data collections at key developmental time points are planned. Overall recruitment of the AOB cohort as well as observational data collection procedures during the perinatal period and early childhood are described in turn below.
This study was approved by the Child Health Research Office and the Conjoint Health Research Ethics Board of the Faculties of Medicine, Nursing, and Kinesiology, University of Calgary, and the Affiliated Teaching Institutions (Ethics ID 20821 and 22821). Participants provided consent at the time of recruitment and were provided copies of the consent form for their records.
Data collection (perinatal period)
Information retrieved from hospital and medical records in the AOB study
Smoking, drug dependent
Pre-existing diabetes, heart disease, hypertension
Chronic renal disease, other chronic disorder, auto immune conditions
Maternal past pregnancy history
Previous term births, past preterm birth, previous preterm deliveries
Number of previous c-sections
Abortion, stillbirth(s), neonatal death, major congenital anomaly
History of intrauterine growth restriction, SGA, LGA
Date admitted to labour and delivery
Maternal height <=152cm, maternal weight (<=45Kg, >=91Kg), poor weight gain
Antepartum risk score
Infection in pregnancy (GBS, HIV, HepB, other), fever, UTI
Poly/oligo, ROM <37 wks, bleeding
Pregnancy induced hypertension, gestational diabetes
Cerclage, pre-eclampsia, eclampsia, abruption, prolonged premature rupture of membranes, placenta previa,
Intrauterine growth restriction, polyhydramnios, chorioamnionitis
Site, type of delivery provider
Multiple pregnancy, maternal age at delivery, gestation, pregnancy >=41 weeks
Admitted for elect c-section, reason for operative delivery
Indication for induction, cervical dilatation at presentation, type of delivery, delivery mode
(Fetal) presentation in labour, trial of labour
Method of induction (oxytocin, artificial rupture of membranes, other)
Narcotics in labour, epidural in labour, Antenatal steroids, use of intrapartum antibiotics
Second stage (minutes), third stage (minutes)
Neonatal gender, birth weight, date/time, disposition
5 minute Apgar score
NICU admission, congenital anomaly
Maternal discharge date, maternal discharge disposition, length of stay
Breastfeeding at discharge
The mailed questionnaire packages included an information letter, consent form, contact information form, questionnaire, and postage pre-paid return envelope. The participants were asked to complete the first questionnaire at recruitment (before 25 weeks gestation), the second between 34-36 weeks gestation, and the third at 4 months postpartum. The questionnaires were returned to the research team by regular post. Trained research assistants contacted the participants if data were missing or clarification of responses was required. Participants who failed to return their questionnaire within three weeks were contacted by telephone and/or e-mail and reminded to complete the questionnaire; multiple attempts were made until the participant was contacted and provided the opportunity for a repeat mail-out or to complete the questionnaire over the telephone. After completion and return of their questionnaires at each time point, the participants were provided with a token of appreciation such as library and grocery store gift cards. In order to keep participants engaged and updated, congratulation cards were sent after the birth of their baby, as well as newsletters semi-annually containing such information as project progress and findings (e.g., most popular baby names), preliminary results and research team member profiles.
All raw data was scanned into Teleform (Version 10.1) and went through a verification process to improve accuracy. Data was exported and cleaned according to data cleaning guidelines, including data coding, frequency editing, and cross-sectional and longitudinal logical editing . Information across the three time points was linked according to a unique identifier that was assigned to each participant at study entry, preserving participant confidentiality. Information from medical charts was linked with questionnaire data by means of personal health numbers. Questionnaire and medical data were stored separately from participant data, the latter which include personal information such as name, address, and personal health number. This separation acts to set up a central barrier between administrative data needed for conducting the study and anonymised data needed to answer the research questions. Both hard copies and electronic copies of data are stored in a secure environment and adhere to security and confidentiality protocol as per the institutional ethics board and recommended guidelines .
Data collection (early childhood)
For each follow-up data collection wave in early childhood (12 months, 24 months, and 36 months), the AOB study team developed a 20 page questionnaire to measure domains of maternal physical and mental health, parenting, health care utilization, and family well-being. Specific questions and standardized tools to assess child developmental outcomes and milestones were also administered. In order to understand trajectories of development, the same construct (e.g., maternal depression) was assessed across time, using the same tool if appropriate. Furthermore, relevant domains of functioning at each time point were assessed. For example, questions regarding work-life balance/return to work and separation anxiety were asked at the 12 month data collection time point, and questions regarding child behaviour and oral health were deemed important for the 36 month follow-up. Outcomes of interest that will be measured in the AOB study across time will include those relevant to population health such as obesity, injuries, recreation, chronic/inflammatory disease, and developmental disorders. Planned domains for a 5 and 8 year follow-up also include recreation, screen time, sleep, and oral health, among others. Detailed in-home anthropometric and developmental assessments, as well as DNA collections are also planned for in subsequent follow-up data collections.
Characteristics of the AOB participants
Demographic characteristics of the AOB study participants
Maternal age at delivery (n=2670)
Marital status (n=3354)
High school or less
Some or completed university/college
Some or completed grad school
$40,000 - $79,000
Born in Canada (n=3360)
Pregnancy and labour/delivery characteristics of the AOB study participants
Pregnancy intention (n=3355)
Trying to get pregnant
Not trying to get pregnant
Feelings about pregnancy (n=3348)
Weight gain during pregnancy a (n=3002)
Method of delivery (n=3055)
Gestational age (n=3032)
Small for Gestational Age (singletons; n=2836)
Large for Gestational Age (singletons; n=2836)
Breastfeeding initiation (n=3057)
Exclusive breastfeeding at 1 wk (n=2969) b
Exclusive breastfeeding at 4-months (n=2976) b
Psychosocial characteristics of the AOB study participants
Depression, EPDSa ≥13 (n=3384)
Anxiety, SAIb≥ 40 (n=3363)
Stress, PSSc 80th percentile (n=3376)
Social support, MOSd total ≤ 69 (n=3379)
Optimism, LOT-Re 20th percentile (n=2925)
4 months postpartum
Depression, EPDSa≥13 (n=3041)
Anxiety, SAIb ≥ 40 (n=2942)
Stress, PSSc 80th percentile (n=3004)
Social support, MOSd total ≤ 69 (n=3012)
Parenting Morale Index, PMIf 20th percentile (n=2931)
Low parenting morale
High parenting morale
Psychosocial characteristics in the AOB cohort were assessed using standardized tools (see additional file 1). Prenatal psychosocial health was operationalized as scoring in the excessive symptom range (high or low depending on the construct) at one or both of the prenatal data collection time points. Women in the AOB cohort reported prevalences of prenatal depression, anxiety, and stress of 12%, 28%, and 31%, respectively. At 4 months postpartum, the rates were lower, at 5% for depression, 15% for anxiety, and 24% for stress. Perceived social support remained high at both time points (>80%) and the majority of women reported high optimism (80%) and parenting morale (83%) (Table 4).
Characteristics of discontinued participants
Comparison between AOB discontinuersa and AOB continuersb
High school or less
More than high school
Born in Canada
Depression in early pregnancy
Anxiety in early pregnancy
Stress in early pregnancy
Social support in early pregnancy
Feelings about pregnancy
Comparison to the target population
Comparison of AOB participants to MESa participants
Pre-pregnancy BMI (mean)
Number of prenatal care visits (mean)
Gestational age at first prenatal care visit (mean)
Initiated prenatal care in first trimester (<14 weeks)
First ultrasound <18weeks
Attended prenatal or childbirth education classes
Satisfied with timing of pregnancy
Feeling happyd upon realization of pregnancy
Intended to breastfeed
Delivery and postpartum experiences
Preterm birth rate
Caesarean section delivery
Short length of maternal stay in hospital
Vaginal (<2 days)
Caesarean section (<4 days)
Scoring ≥13 on Edinburgh Postnatal Depression Scale
Rated postpartum health as very good or excellent
Postpartum BMI (mean)
Although the MES may be a less than ideal comparison for representativeness, given that AOB and MES employ different sampling strategies (i.e., stratified sampling in MES, non-stratified sampling in AOB), the range of factors assessed in the MES allows for a wide range of comparisons, beyond sociodemographic characteristics and birth indicators. Further comparisons with other data sources at the local and provincial level such as administrative data on perinatal health and Census community profiles during or close to the study time period suggest that the AOB participants are generally representative of the pregnancy and parenting population at the local (city) and provincial levels. For example, the average age of women in Calgary and Alberta giving birth in 2010 was 30.8 and 29.5 years . In the AOB study, the average age at delivery was 31.2 (SD=4.4). Approximately one-quarter of women in Calgary were foreign-born and one-quarter were a visible minority according to the Canadian Census , with similar percentages seen in the AOB study (Table 2). Furthermore, 53% of women in the AOB study report a household income of over 100K, which aligns with the median income of couple families according to recent statistics from Statistics Canada for 2010 (approximately 97K) .
Comparison to perinatal surveillance data
Recent data on perinatal indicators  report a singleton preterm birth rate of 7.9% and 8.8% in Canada and Alberta, respectively. The AOB preterm birth rate for singletons of 7.3% falls below both the provincial and national rates; on the other hand, the AOB SGA rate of 10.6% is greater than the corresponding provincial and national rates. Taken together, this suggests possible misclassification of both birth weight and gestational age data according to self-report. Validation work with medical charts for important labour and delivery outcomes has been completed and is described elsewhere in this issue . Although relatively high agreement was found between the two data sources for select perinatal indicators , misclassification cannot be ruled out when comparing study rates to perinatal surveillance data. Finally, mothers in the AOB cohort had much higher breastfeeding initiation rates than those reported for both Canada and Alberta (98% vs. 87% and 91%, respectively).
Emerging evidence recognizes the importance of prenatal and early life events on the long term development of children [29, 30]. The AOB cohort has the unique opportunity to inform complex questions about the relationship between biology, early experiences, and developmental outcomes, and to contribute to a better understanding of the current circumstances of importance to families for stakeholders, policy and decision makers. An informed picture of the early determinants of childhood development and family outcomes is potentially important for not only prevention of disability and ill-health but also in developing an understanding of mechanisms underlying associations between early and later life outcomes (e.g., early socioeconomic status (SES) as a predictor of childhood intelligence and its role in explaining the association between childhood intelligence and risk for adult disease; ). Future studies examining associations between risk factors and later life outcomes must ensure adequate control for potential confounders. Such early life determinants of such risk factors, that are outcomes in themselves, require elucidation and adequate measurement. A key advantage of the AOB cohort, like some other established longitudinal cohorts (e.g., ALSPAC, Generation-R), is that its prospective data collection began in pregnancy. Although birth cohorts and cohorts that begin in early childhood are important sources for life course research, pregnancy cohorts are well positioned to overcome methodological limitations such as recall bias for exposures and confounding variables in pregnancy. Common to all cohort studies, sample attrition over time may be a source of selection bias for the AOB cohort (see below). Although the AOB cohort demonstrated a retention rate of 90% of participants between the first and third questionnaire, there was an 86% response rate for the 12 month data collection. Although this latter rate is still high, the decrease across time serves as a reminder that intensive participant engagement is an important component for ongoing cohort maintenance and follow-up.
Tracking typical and atypical trajectories of child development as well as risk factors and effect modifiers is important for the development of preventative strategies. We have incorporated assessment tools to screen for atypical development as part of the 12, 24, and 36 month follow-up data collections. For example, the MacAurthur-Bates Communicative Development Inventories  are included during follow-up to identify those children at risk for language delay. To our knowledge, no previous population-based cohort exists of this size that incorporates three assessments of atypical child development coupled with rich maternal data and other gold standard tools. Follow-up data collections will also allow for examining typical and atypical trajectories of maternal and family well-being after the birth of a new baby. Longitudinal data analyses will be performed to examine precursors and outcomes of trajectories. We will also track outcomes as part of surveillance undertaking for the AOB cohort. Some specific projects that will use longitudinal data include: examining early risk factors for language delay; intergenerational transmission of psychosocial risk; and long-term outcomes for late-preterm infants and their families.
Threats to validity
A main source of potential bias for longitudinal studies is that due to non-response; pregnancy and birth cohorts are no exception. Non-response can affect both external and internal validity. In general, non-response can take three forms: unit non-response, or absence of the target sample at study outset; temporary or wave non-response; and permanent non-response, commonly referred to as attrition . An analysis of unit non-response generally comprises a comparison of the study population to the eligible or target population, and may derive from previous collection of minimal data sets on individuals who either refused to participate or were missed , or the use of administrative data sources with total population coverage of births or pregnancies [14, 18]. Temporary and permanent non-response can be assessed if baseline information is collected before drop-out; our comparison between continuers and discontinuers is an example of an assessment of this type of non-response and threat to validity. In line with other cohort studies, non-continuers in the AOB were more likely to report poorer mental health and lower socioeconomic status [35–37]. We will continue to examine the characteristics of discontinuers across time as selection bias due to attrition may become an increasing threat to validity, in particular when examining lifecourse associations. In the AOB cohort, other potential sources of bias such as information bias (e.g., misclassification bias, recall bias) and bias due to confounding are kept to a minimum due to the prospective nature of data collection, use of standardized tools, and assessment across a range of variables including different data sources. However, we cannot discount the possibility that reporting bias due self-report will remain a potential threat to validity, and, where possible, we will utilize medical records and administrative sources of information and/or conduct validation analyses between different data sources to maintain internal validity. Although vulnerable women may be at higher risk of discontinuation, variability in ethnicity, SES etc. is present, and tends to reflect the urban Calgary parenting population, which allows for examining associations for these factors, maintaining internal validity at the expense of external validity (generalizability).
The AOB cohort, in general, is representative of the pregnant and parenting population in a Canadian urban setting, Important research and policy questions are currently under examination, results which have the potential to add to the evidence base and inform decision makers about the health and well-being of pregnant women and their families. The AOB cohort will continue to be a significant Alberta resource that will have implications far beyond its local roots.
List of abbreviations used
All Our Babies
Avon Longitudinal Study of Parents and Children
Maternity Experiences Survey
We are extremely grateful to the participants involved in the All Our Babies cohort, and to the All Our Babies staff and research team. We are extremely grateful to the investigators, co-ordinators, research assistants, graduate and undergraduate students, volunteers, clerical staff and managers. Alberta Innovates - Health Solutions, formerly the Alberta Heritage Foundation for Medical Research, as part of the Preterm Birth and Healthy Outcomes Team Interdisciplinary Team Grant (#200700595), Three Cheers for the Early Years, Alberta Health Services and the Alberta Children’s Hospital Foundation have provided support for the study. The University of Calgary has provided trainee salary support. Alberta Innovates Health Solutions provided funding towards this cohort and salary support for Suzanne Tough. Additional funding from the Alberta Centre for Child, Family, and Community Research (postdoctoral fellowship) for Sheila McDonald assisted with the analysis of data presented in this manuscript.
This article has been published as part of BMC Pregnancy and Childbirth Volume 13 Supplement 1, 2013: Preterm Birth: Interdisciplinary Research from the Preterm Birth and Healthy Outcomes Team (PreHOT). The full contents of the supplement are available online athttp://www.biomedcentral.com/bmcpregnancychildbirth/supplements/13/S1.
All of the publication fees will be funded by the Preterm Birth and Healthy Outcomes Team Interdisciplinary Team Grant (#200700595) from Alberta Innovates - Health Solutions, formerly the Alberta Heritage Foundation for Medical Research.
- Manolio TA, Bailey-Wilson JE, Collins FS: Genes, environment and the value of prospective cohort studies. Nat Rev Genet. 2006, 7 (10): 812-820. 10.1038/nrg1919.View ArticlePubMedGoogle Scholar
- Bao W, Threefoot SA, Srinivasan SR, Berenson GS: Essential hypertension predicted by tracking of elevated blood pressure from childhood to adulthood: the Bogalusa Heart Study. Am J Hypertens. 1995, 8 (7): 657-665. 10.1016/0895-7061(95)00116-7.View ArticlePubMedGoogle Scholar
- Barker DJ, Osmond C: Infant mortality, childhood nutrition, and ischaemic heart disease in England and Wales. Lancet. 1986, 1 (8489): 1077-1081.View ArticlePubMedGoogle Scholar
- Barker DJ: Maternal nutrition, fetal nutrition, and disease in later life. Nutrition. 1997, 13 (9): 807-813. 10.1016/S0899-9007(97)00193-7.View ArticlePubMedGoogle Scholar
- Gillman MW: Developmental origins of health and disease. N Engl J Med. 2005, 353 (17): 1848-1850. 10.1056/NEJMe058187.PubMed CentralView ArticlePubMedGoogle Scholar
- Gluckman PD, Hanson MA, Cooper C, Thornburg KL: Effect of in utero and early-life conditions on adult health and disease. N Engl J Med. 2008, 359 (1): 61-73. 10.1056/NEJMra0708473.PubMed CentralView ArticlePubMedGoogle Scholar
- Hofstra MB, van der Ende J, Verhulst FC: Child and adolescent problems predict DSM-IV disorders in adulthood: a 14-year follow-up of a Dutch epidemiological sample. J Am Acad Child Adolesc Psychiatry. 2002, 41 (2): 182-189. 10.1097/00004583-200202000-00012.View ArticlePubMedGoogle Scholar
- Kuh D, Ben-Shlomo Y, Lynch J, Hallqvist J, Power C: Life course epidemiology. J Epidemiol Community Health. 2003, 57 (10): 778-783. 10.1136/jech.57.10.778.PubMed CentralView ArticlePubMedGoogle Scholar
- Golding J, Jones R, Brune M-N, Pronczuk J: Why carry out a longitudinal birth survey?. Paediatr Perinat Epidemiol. 2009, 23 (Suppl 1): 1-14.View ArticlePubMedGoogle Scholar
- Jaddoe VWV, Witteman JCM: Hypotheses on the fetal origins of adult diseases: contributions of epidemiological studies. Eur J Epidemiol. 2006, 21 (2): 91-102. 10.1007/s10654-005-5924-5.View ArticlePubMedGoogle Scholar
- Wadsworth MEJ, Butterworth SL, Hardy RJ, Kuh DJ, Richards M, Langenberg C, Hilder WS, Connor M: The life course prospective design: an example of benefits and problems associated with study longevity. Soc Sci Med. 2003, 57 (11): 2193-2205. 10.1016/S0277-9536(03)00083-2.View ArticlePubMedGoogle Scholar
- Knox SS, Echeveria D: Methodological issues related to longitudinal epidemiological assessment of developmental trajectories in children. J Epidemiol Community Health. 2009, 63 (Suppl 1): i1-3. 10.1136/jech.2007.070813.View ArticlePubMedGoogle Scholar
- Golding J, Pembrey M, Jones R, Team AS: ALSPAC--the Avon Longitudinal Study of Parents and Children. I. Study methodology. Paediatr Perinat Epidemiol. 2001, 15 (1): 74-87. 10.1046/j.1365-3016.2001.00325.x.View ArticlePubMedGoogle Scholar
- Jaddoe VWV, Mackenbach JP, Moll HA, Steegers EAP, Tiemeier H, Verhulst FC, Witteman JCM, Hofman A: The Generation R Study: Design and cohort profile. Eur J Epidemiol. 2006, 21 (6): 475-484. 10.1007/s10654-006-9022-0.View ArticlePubMedGoogle Scholar
- Olsen J, Melbye M, Olsen SF, Sorensen TI, Aaby P, Andersen AM, Taxbol D, Hansen KD, Juhl M, Schow TB, Sorensen HT, Andresen J, Mortensen EL, Olesen AW, Sondergaard C: The Danish National Birth Cohort--its background, structure and aim. Scand J Public Health. 2001, 29 (4): 300-307.View ArticlePubMedGoogle Scholar
- Plewis I: Millennium Cohort Study First Survey: Technical Report on Sampling. 2004, Centre for Longitudinal Studies. London, UKGoogle Scholar
- Landrigan PJ, Trasande L, Thorpe LE, Gwynn C, Lioy PJ, D'Alton ME, Lipkind HS, Swanson J, Wadhwa PD, Clark EB, Rauh VA, Perera FP, Susser E: The National Children's Study: a 21-year prospective study of 100,000 American children. Pediatrics. 2006, 118 (5): 2173-2186. 10.1542/peds.2006-0360.View ArticlePubMedGoogle Scholar
- Walker MC, Finkelstein SA, Rennicks White R, Shachkina S, Smith GN, Wen SW, Rodger M: The Ottawa and Kingston (OaK) Birth Cohort: development and achievements. J Obstet Gynaecol Can. 2011, 33 (11): 1124-1133.PubMedGoogle Scholar
- Gracie SK, Lyon AW, Kehler HL, Pennell CE, Dolan SM, McNeil DA, Siever JE, McDonald SW, Bocking AD, Lye SJ, Hegadoren KM, Olson DM, Tough SC: All Our Babies Cohort Study: recruitment of a cohort to predict women at risk of preterm birth through the examination of gene expression profiles and the environment. BMC Pregnancy Childbirth. 2010, 10: 87-10.1186/1471-2393-10-87.PubMed CentralView ArticlePubMedGoogle Scholar
- Golding J: Data organisation and preparation for statistical analysis in a longitudinal birth cohort. Paediatr Perinat Epidemiol. 2009, 23 (Suppl 1): 219-225.View ArticlePubMedGoogle Scholar
- Canadian Gestational Weight Gain Recommendations. [http://www.hc-sc.gc.ca/fn-an/nutrition/prenatal/qa-gest-gros-qr-eng.php]
- Public Health Agency of Canada: What Mothers Say: The Canadian Maternity Experiences Survey. 2009, OttawaGoogle Scholar
- Dzakpasu S, Kaczorowski J, Chalmers B, Heaman M, Duggan J, Neusy E, Maternity Experiences Study Group of the Canadian Perinatal Surveillance System, Public Health Agency of Canada: The Canadian Maternity Experiences Survey: design and methods. J Obstet Gynaecol Can. 2008, 30 (3): 207-216.PubMedGoogle Scholar
- Alberta Reproductive Health Report Working Group: Alberta Reproductive Health: Pregnancies and Births Table Update 2011. 2011, Edmonton, ABGoogle Scholar
- Statistics Canada: Calgary, Alberta (Code4806016) (table). 2006 Community Profiles. 2006 Census. Statistics Canada Catalogue no. 92-591-XWE. 2007, Ottawa, [http://www12.statcan.ca/census-recensement/2006/dp-pd/prof/92-591/index.cfm?Lang=E]Google Scholar
- Statistics Canada: Family income and income of individuals, related variable: Sub-provincial data, 2010. Statistics Canada Catalogue no. 11-001-X. 2012, Ottawa, [http://www.statcan.gc.ca/daily-quotidien/120627/dq120627b-eng.pdf]Google Scholar
- Public Health Agency of Canada: Perinatal Health Indicators for Canada 2011. 2012, OttawaGoogle Scholar
- Bat-Erdene U, Metcalfe A, McDonald SW, Tough SC: Validation of Canadian mothers’ recall of events in labour and delivery with medical health records. BMC Pregnancy Childbirth. 2013, 13 (Suppl 1): S3-10.1186/1471-2393-13-S1-S3.PubMed CentralView ArticlePubMedGoogle Scholar
- Hertzman C, Power C: Child development as a determinant of health across the life course. Current Paediatrics. 2004, 14 (5): 438-443. 10.1016/j.cupe.2004.05.008.View ArticleGoogle Scholar
- Shonkoff JP, Garner AS, Committee on Psychosocial Aspects of C, Family H, Committee on Early Childhood A, Dependent C, Section on D, Behavioral P: The lifelong effects of early childhood adversity and toxic stress. Pediatrics. 2012, 129 (1): e232-246. 10.1542/peds.2011-2663.View ArticlePubMedGoogle Scholar
- Lawlor DA, Najman JM, Batty GD, O'Callaghan MJ, Williams GM, Bor W: Early life predictors of childhood intelligence: findings from the Mater-University study of pregnancy and its outcomes. Paediatr Perinat Epidemiol. 2006, 20 (2): 148-162. 10.1111/j.1365-3016.2006.00704.x.View ArticlePubMedGoogle Scholar
- Fenson L, Marchman VA, Thal DJ, Dale PS, Reznick JS, Bates E: MacArthur-Bates Communicative Development Inventories. 2007, Baltimore, MD: Paul H. Brookes Publishing Co, SecondGoogle Scholar
- Hawkes D, Plewis I: Modelling non-response in the National Child Development Study. Journal of the Royal Statistical Society. 2006, 169: 479-491. 10.1111/j.1467-985X.2006.00401.x.View ArticleGoogle Scholar
- Ebner A, Thyrian JR, Lange A, Lingnau M-L, Scheler-Hofmann M, Rosskopf D, Zygmunt M, Haas J-P, Hoffmann W, Fusch C: Survey of Neonates in Pomerania (SNiP): a population-based birth study--objectives, design and population coverage. Paediatr Perinat Epidemiol. 2010, 24 (2): 190-199. 10.1111/j.1365-3016.2009.01078.x.View ArticlePubMedGoogle Scholar
- Cartwright A: Who responds to postal questionnaires?. J Epidemiol Community Health. 1986, 40 (3): 267-273. 10.1136/jech.40.3.267.PubMed CentralView ArticlePubMedGoogle Scholar
- Golding J, Birmingham K: Enrollment and response rates in a longitudinal birth cohort. Paediatr Perinat Epidemiol. 2009, 23 (Suppl 1): 73-85.View ArticlePubMedGoogle Scholar
- Hapgood C, Elkind G: Refusal to participate: effects on sample selection in a longitudinal study of postnatal mood. Journal of Psychosomatic Obstetrics and Gynaecology (Suppl ). 1989, 10: 89-97. 10.3109/01674828909016681.View ArticleGoogle Scholar
- Sherbourne CD, Stewart AL: The MOS social support survey. Soc Sci Med. 1991, 32 (6): 705-714. 10.1016/0277-9536(91)90150-B.View ArticlePubMedGoogle Scholar
- The SF-12®: An Even Shorter Health Survey. [http://www.sf-36.org/tools/sf12.shtml/]
- Cox JL, Holden JM, Sagovsky R: Detection of postnatal depression. Development of the 10-item Edinburgh Postnatal Depression Scale. Br J Psychiatry. 1987, 150: 782-786. 10.1192/bjp.150.6.782.View ArticlePubMedGoogle Scholar
- Spielberger C, Gorsuch R: Test Manual for the State-Trait Anxiety Inventory. 1970, Palo Alto, California: Consulting Psychologist's PressGoogle Scholar
- Cohen S, Kamarck T, Mermelstein R: A global measure of perceived stress. J Health Soc Behav. 1983, 24 (4): 385-396. 10.2307/2136404.View ArticlePubMedGoogle Scholar
- Scheier MF, Carver CS, Bridges MW: Distinguishing optimism from neuroticism (and trait anxiety, self-mastery, and self-esteem): a reevaluation of the Life Orientation Test. J Pers Soc Psychol. 1994, 67 (6): 1063-1078.View ArticlePubMedGoogle Scholar
- Trute B, Hiebert-Murphy D: Predicting family adjustment and parenting stress in childhood disability services using brief assessment tools. Journal of Intellectual & Developmental Disability. 2005, 24: 352-396.Google Scholar
- Sokol RJ, Martier SS, Ager JW: The T-ACE questions: practical prenatal detection of risk-drinking. Am J Obstet Gynecol. 1989, 160 (4): 863-868. 10.1016/0002-9378(89)90302-5.View ArticlePubMedGoogle Scholar
- Ramsay M, Martel C, Porporino M, Zygmuntowicz C: The Montreal Children's Hospital Feeding Scale: A brief bilingual screening tool for identifying feeding problems. Paediatr child health. 2011, 16 (3): 147-e117.PubMed CentralPubMedGoogle Scholar
- Glascoe F, Brigance A: Brigance Infant and Toddler Screen: Parent-Child Interactions Form. 2002, North Billerica, MA: Curriculum AssociatesGoogle Scholar
- Glascoe FP, Leew S: Parenting behaviors, perceptions, and psychosocial risk: impacts on young children's development. Pediatrics. 2010, 125 (2): 313-319. 10.1542/peds.2008-3129.View ArticlePubMedGoogle Scholar
- Boivin M, Perusse D, Dionne G, Saysset V, Zoccolillo M, Tarabulsy GM, Tremblay N, Tremblay RE: The genetic-environmental etiology of parents' perceptions and self-assessed behaviours toward their 5-month-old infants in a large twin and singleton sample. J Child Psychol Psychiatry. 2005, 46 (6): 612-630. 10.1111/j.1469-7610.2004.00375.x.View ArticlePubMedGoogle Scholar