571169
research-article2015
ANP0010.1177/0004867415571169Australian & New Zealand Journal of PsychiatryHutchinson et al.
Viewpoint Australian & New Zealand Journal of Psychiatry 2015, Vol. 49(4) 317–323 DOI: 10.1177/0004867415571169
How can data harmonisation benefit mental health research? An example of The Cannabis Cohorts Research Consortium
© The Royal Australian and New Zealand College of Psychiatrists 2015 Reprints and permissions: sagepub.co.uk/journalsPermissions.nav anp.sagepub.com
Delyse M Hutchinson1,2,3, Edmund Silins1, Richard P Mattick1, George C Patton3,4, David M Fergusson5, Reza Hayatbakhsh6, John W Toumbourou2,3, Craig A Olsson2,3,4, Jake M Najman7, Elizabeth Spry3, Robert J Tait8, Louisa Degenhardt1, Wendy Swift1, Peter Butterworth9 and L John Horwood4 for the Cannabis Cohorts Research Consortium
Despite significant research on cannabis use, less is known about antecedents and effects of less prevalent patterns of use, such as early daily or dependent use. Cohort studies typically have insufficient samples (i.e. small cell sizes) and lack the statistical power to examine relationships between regular use and low prevalence adverse outcomes (e.g. suicide). Consequently, individual cohort studies offer limited opportunity to explore the causes and consequences of cannabis use patterns which confer the greatest burden of disease, namely, daily use and dependence (Curran and Hussong, 2009; Hofer and Piccinin, 2009). One approach to addressing these limitations is to invest in new, large, prospective studies, capable of providing the power needed to examine less frequent events. However, such studies take decades to mature, are extremely costly and necessarily delay important health knowledge reaching the research field, clinicians, decision makers in government and the general population. One alternative is to make use of the available data by harmonising and pooling individual participant data across individual cohorts. A multi-cohort consortium approach provides a number of potential advantages including: (1) efficiency in the use of existing data, time and resources; (2) the capacity to bring
together expert knowledge from across a range of disciplinary boundaries; (3) increased opportunity for knowledge translation and dissemination; (4) the increased generalisability afforded by combining data collected by different researchers on different samples; and (5) the opportunity to combine data from a number of studies to answer questions that cannot be answered in individual cohorts. We provide an example of such an approach.
The Cannabis Cohorts Research Consortium (CCRC): An example of a multi-cohort consortium approach The bi-national Cannabis Cohorts Research Consortium (CCRC) is an example of a research effort using integrated data analysis across multiple Australasian cohorts. The CCRC is a multi-organisational and multidisciplinary international collaboration that brings together a number of the most mature longitudinal studies of child and adolescent development across Australia and New Zealand, including: the Australian Temperament Project (30 years/15 waves), the Christchurch Health and Development Study (37 years/23 waves), the Mater
Hospital and University of Queensland Study of Pregnancy (33 years/8
1National
Drug and Alcohol Research Centre, UNSW Australia, Sydney, Australia 2Centre for Social and Early Emotional Development, School of Psychology, Deakin University, Australia 3Centre for Adolescent Health, Murdoch Childrens Research Institute, The Royal Children’s Hospital Melbourne, Melbourne, Australia 4Psychological Sciences and Department of Paediatrics, The University of Melbourne, Parkville, Australia 5Christchurch Health and Development Study, Department of Psychological Medicine, University of Otago, Christchurch, New Zealand 6School of Population Health, University of Queensland, Brisbane, QLD, Australia 7Schools of Population Health and Social Science, University of Queensland, Brisbane, Australia 8National Drug Research Institute, Faculty of Health Sciences, Curtin University, Perth, Australia 9Centre for Research on Ageing, Health and Wellbeing, The Australian National University, Canberra, Australia And the Cannabis Cohorts Research Consortium (CCRC) Corresponding author: Edmund Silins, Research Fellow, National Drug and Alcohol Research Centre, UNSW Australia, 22-32 King St, Randwick, Sydney, NSW, 2052, Australia. Email: [email protected]
Australian & New Zealand Journal of Psychiatry, 49(4)
318 waves), the Personality and Total Health Through Life Project (15 years/4 waves), the Victorian Adolescent Health Cohort Study (20 years/10 waves) and the Western Australian Pregnancy Cohort (Raine) Study (25 years/11 waves). The CCRC was formed in 2006 and is coordinated by the National Drug and Alcohol Research Centre (NDARC) at UNSW Australia. The CCRC stemmed from a recognised need to better understand the link between cannabis use (particularly rarer patterns of high use) and other substance use, mental health problems and psychosocial outcomes. The CCRC has first focused on cannabis use across adolescence and young adulthood, in particular the impact of regular patterns of use (i.e. daily and dependent use). Here, we describe an approach to developing well-powered samples for the study of infrequent exposures and outcomes.
What cohorts are involved in the CCRC? The Consortium brings together researchers from some of the largest and longest running longitudinal studies of health and wellbeing internationally. Details of the cohorts presently are: 1. The Australian Temperament Project (ATP) (Prior et al., 2000; Vassallo and Sanson, 2013) is a longitudinal study of social and emotional development that commenced in 1983 as a sample of 2443 infants (aged 4–8 months) and their parents. Maternal and child health nurses at Infant Welfare Centres, which successfully contacted 94% of families with a new infant in the State of Victoria, Australia, assisted with enrolling families for the study. Nurses distributed approximately 3000 questionnaires, and 2443 (81%) usable questionnaires were returned. These 2443 formed the initial sample.
ANZJP Perspectives Follow-up: The ATP has been studied on a total of 15 occasions: in childhood (4 months, 1, 2, 3, 5, 7, 9, 11, 12 years), adolescence (13, 15, 17, 19 years) and young adulthood (24, 28 years). 2. The Christchurch Health and Development Study (CHDS) (Fergusson and Horwood, 2001; Fergusson et al., 1989) is a longitudinal study of a birth cohort of 1265 children born in the Christchurch, New Zealand urban region in 1977. These children included 97% of all live births occurring during the recruitment period. Follow-up: The cohort has now been studied on a total of 23 occasions (birth, 4 months, yearly between 1–16 years, and then at 18, 21, 25, 30 and 35 years). 3. The Mater Hospital and University of Queensland Study of Pregnancy (MUSP) Between 1981 and 1983, 8556 consecutive pregnant women attending for their first clinic visit at average 18 weeks’ gestation at the Mater Misericordiae Mothers’ Hospital in Brisbane, Australia, were invited to participate in this longitudinal birth cohort (Keeping et al., 1989). Of those invited, 8458 (99%) agreed to participate in the study and 7223 gave birth to a live singleton child. The response proportion was 84%. Follow-up: The cohort has now been studied on eight occasions (18 weeks gestation, birth, 4, 5, 14, 21, 27 and 30 years). 4. The Personality and Total Health (PATH) Through Life Project (Anstey et al., 2012) is a longitudinal cohort study that commenced in 1999 with the recruitment of 7485 young (age 20–24 years at baseline, n = 2404), midlife (age 40–44 years at baseline, n = 2530) and older (age 60–64 years at baseline, n = 2551) adults randomly sampled from the electoral roll of the Australian Capital Territory and the nearby city of Queanbeyan, Australia. To align with the focus of the Consortium,
Australian & New Zealand Journal of Psychiatry, 49(4)
only the youngest PATH cohort was included in the CCRC. The response rate among those contacted who were in the target age range (20–24 years) and still resident in the area was 58.6%. Follow-up: The cohort has been assessed on four occasions between 1999 and 2011 (age 20– 24 years at baseline, 24–28 years, 28–32 years and 32–38 years). 5. The Victorian Adolescent Health Cohort Study (VAHCS) (Patton et al., 2007) is a longitudinal study of a representative sample of midsecondary school adolescents in Victoria, Australia. In 1992, participants were recruited via schools at the end of Year 9 (wave 1, mean age 14.9 years) or the start of Year 10 (wave 2, mean age 15.5 years). Of the invited sample of 2032 students, 1943 (96%) were assessed at least once during the first six waves. Follow-up: Participants were assessed at six-monthly intervals on four further occasions during adolescence: wave 3 (mean age 15.9 years) – wave 6 (mean age 17.4 years); with a further four follow-ups in young adulthood: wave 7 (mean age 20.7 years) – wave 10 (mean age 35 years). 6. The Western Australian Pregnancy Cohort (Raine) Study (Newnham et al., 1993; McNight et al., 2012) commenced in 1989. An estimated 3222 (Mountain, 2013, personal communication) pregnant women at around 18 weeks gestation from King Edward Memorial Hospital in Perth, Western Australia, were invited to participate. Of these, 2900 pregnant women were recruited. These pregnancies resulted in 2868 children recruited into the cohort. Follow-up: Information was collected from the mothers during pregnancy and parent/guardian at each follow up until the participants were 17 years of age. Participants were assessed at birth and at ages 1, 2, 3, 5, 8, 10, 14, 17, 20 and most recently 23 years of age.
319
Hutchinson et al.
How has the CCRC pooled data across cohorts? There are two major approaches to data pooling. The first, commonly used method, combines study level results meta-analytically. This approach is relatively straightforward to apply when there is a clear research hypothesis, the exposure and outcome variables are clearly and consistently specified within and across studies, and the measure of effect size can be easily interpreted (Curran and Hussong, 2009). However, because meta-analyses are based on combining estimates at the study level, rather than at the individual participant level, investigation is limited by low sample size and power. That is, often the unit of analysis in meta-analysis is the study, when more power can be derived if the unit of analysis is the study participant. The second approach to data pooling, pooling of participant-level data, is less commonly used because gaining access to study participant data is more complicated (ethically and practically) than combining available studylevel results. It is, however, ideally suited to investigations of low frequency exposures and/or outcomes such as rarer behaviours (e.g. regular drug use) or genetic variants. The approach involves developing common scales of measurement across cohorts (harmonisation) and pooling participant-level harmonised data from multiple cohorts to create a single (large) integrated dataset of participants rather than studies. Data harmonisation and pooling allows models to be fitted directly to participant-level data and, therefore, provides a number of advantages over study-level meta-analysis. The major advantages of data harmonisation are that it (Curran and Hussong, 2009): 1. provides increased sample size which enables more powerful examination of less frequent exposures and outcomes; 2. becomes feasible to explicitly model sources of between-study
heterogeneity at the level of the observed data; 3. improves the stability of model estimation and reduces the influence of outliers and study specific characteristics; and 4. provides greater precision, particularly for tests of interaction and subgroup analyses. The CCRC has now developed a harmonised data set with over 20 variables, across four cohorts (ATP, CHDS, MUSP, VAHCS) with more than 7000 respondents, with the purpose of further examining questions around frequent or dependent cannabis use that may not be answered by analyses of individual cohorts. Selected PATH data on cannabis and depression have also been harmonised within this data set (Horwood et al., 2012). We intend to harmonise relevant data from all six cohorts in the CCRC subject to continued funding support. Table 1 provides an overview of the key domains of interest for data harmonisation, the availability of these data across the four main cohorts harmonised to date (ATP, CHDS, MUSP, VAHCS), and where data are available, the measures used in each of these cohort studies. The annual attrition rate across the six cohorts was low; in the range of 0.5–3.9% per annum among the cohorts; the retained (contactable) sample rates at most recent follow-up in the range of 70–96%1. Although all studies showed evidence of some selection bias attributable to sample attrition, analysis of the implications of such biases for study findings have shown these to be minimal (Silins et al., 2014). Table 2 provides descriptive data on demographic and cannabis use characteristics in the harmonised sample.
What has it found? Key findings and publications Findings from the analysis of the integrated data improved knowledge of the relationships between cannabis
use, mental health, other substance use and social development in young people (Horwood et al., 2010, 2012; Silins et al., 2014). For example, the age of initiation of cannabis use was found to be a significant factor in educational attainment, with early use (i.e.
research-article2015
ANP0010.1177/0004867415571169Australian & New Zealand Journal of PsychiatryHutchinson et al.
Viewpoint Australian & New Zealand Journal of Psychiatry 2015, Vol. 49(4) 317–323 DOI: 10.1177/0004867415571169
How can data harmonisation benefit mental health research? An example of The Cannabis Cohorts Research Consortium
© The Royal Australian and New Zealand College of Psychiatrists 2015 Reprints and permissions: sagepub.co.uk/journalsPermissions.nav anp.sagepub.com
Delyse M Hutchinson1,2,3, Edmund Silins1, Richard P Mattick1, George C Patton3,4, David M Fergusson5, Reza Hayatbakhsh6, John W Toumbourou2,3, Craig A Olsson2,3,4, Jake M Najman7, Elizabeth Spry3, Robert J Tait8, Louisa Degenhardt1, Wendy Swift1, Peter Butterworth9 and L John Horwood4 for the Cannabis Cohorts Research Consortium
Despite significant research on cannabis use, less is known about antecedents and effects of less prevalent patterns of use, such as early daily or dependent use. Cohort studies typically have insufficient samples (i.e. small cell sizes) and lack the statistical power to examine relationships between regular use and low prevalence adverse outcomes (e.g. suicide). Consequently, individual cohort studies offer limited opportunity to explore the causes and consequences of cannabis use patterns which confer the greatest burden of disease, namely, daily use and dependence (Curran and Hussong, 2009; Hofer and Piccinin, 2009). One approach to addressing these limitations is to invest in new, large, prospective studies, capable of providing the power needed to examine less frequent events. However, such studies take decades to mature, are extremely costly and necessarily delay important health knowledge reaching the research field, clinicians, decision makers in government and the general population. One alternative is to make use of the available data by harmonising and pooling individual participant data across individual cohorts. A multi-cohort consortium approach provides a number of potential advantages including: (1) efficiency in the use of existing data, time and resources; (2) the capacity to bring
together expert knowledge from across a range of disciplinary boundaries; (3) increased opportunity for knowledge translation and dissemination; (4) the increased generalisability afforded by combining data collected by different researchers on different samples; and (5) the opportunity to combine data from a number of studies to answer questions that cannot be answered in individual cohorts. We provide an example of such an approach.
The Cannabis Cohorts Research Consortium (CCRC): An example of a multi-cohort consortium approach The bi-national Cannabis Cohorts Research Consortium (CCRC) is an example of a research effort using integrated data analysis across multiple Australasian cohorts. The CCRC is a multi-organisational and multidisciplinary international collaboration that brings together a number of the most mature longitudinal studies of child and adolescent development across Australia and New Zealand, including: the Australian Temperament Project (30 years/15 waves), the Christchurch Health and Development Study (37 years/23 waves), the Mater
Hospital and University of Queensland Study of Pregnancy (33 years/8
1National
Drug and Alcohol Research Centre, UNSW Australia, Sydney, Australia 2Centre for Social and Early Emotional Development, School of Psychology, Deakin University, Australia 3Centre for Adolescent Health, Murdoch Childrens Research Institute, The Royal Children’s Hospital Melbourne, Melbourne, Australia 4Psychological Sciences and Department of Paediatrics, The University of Melbourne, Parkville, Australia 5Christchurch Health and Development Study, Department of Psychological Medicine, University of Otago, Christchurch, New Zealand 6School of Population Health, University of Queensland, Brisbane, QLD, Australia 7Schools of Population Health and Social Science, University of Queensland, Brisbane, Australia 8National Drug Research Institute, Faculty of Health Sciences, Curtin University, Perth, Australia 9Centre for Research on Ageing, Health and Wellbeing, The Australian National University, Canberra, Australia And the Cannabis Cohorts Research Consortium (CCRC) Corresponding author: Edmund Silins, Research Fellow, National Drug and Alcohol Research Centre, UNSW Australia, 22-32 King St, Randwick, Sydney, NSW, 2052, Australia. Email: [email protected]
Australian & New Zealand Journal of Psychiatry, 49(4)
318 waves), the Personality and Total Health Through Life Project (15 years/4 waves), the Victorian Adolescent Health Cohort Study (20 years/10 waves) and the Western Australian Pregnancy Cohort (Raine) Study (25 years/11 waves). The CCRC was formed in 2006 and is coordinated by the National Drug and Alcohol Research Centre (NDARC) at UNSW Australia. The CCRC stemmed from a recognised need to better understand the link between cannabis use (particularly rarer patterns of high use) and other substance use, mental health problems and psychosocial outcomes. The CCRC has first focused on cannabis use across adolescence and young adulthood, in particular the impact of regular patterns of use (i.e. daily and dependent use). Here, we describe an approach to developing well-powered samples for the study of infrequent exposures and outcomes.
What cohorts are involved in the CCRC? The Consortium brings together researchers from some of the largest and longest running longitudinal studies of health and wellbeing internationally. Details of the cohorts presently are: 1. The Australian Temperament Project (ATP) (Prior et al., 2000; Vassallo and Sanson, 2013) is a longitudinal study of social and emotional development that commenced in 1983 as a sample of 2443 infants (aged 4–8 months) and their parents. Maternal and child health nurses at Infant Welfare Centres, which successfully contacted 94% of families with a new infant in the State of Victoria, Australia, assisted with enrolling families for the study. Nurses distributed approximately 3000 questionnaires, and 2443 (81%) usable questionnaires were returned. These 2443 formed the initial sample.
ANZJP Perspectives Follow-up: The ATP has been studied on a total of 15 occasions: in childhood (4 months, 1, 2, 3, 5, 7, 9, 11, 12 years), adolescence (13, 15, 17, 19 years) and young adulthood (24, 28 years). 2. The Christchurch Health and Development Study (CHDS) (Fergusson and Horwood, 2001; Fergusson et al., 1989) is a longitudinal study of a birth cohort of 1265 children born in the Christchurch, New Zealand urban region in 1977. These children included 97% of all live births occurring during the recruitment period. Follow-up: The cohort has now been studied on a total of 23 occasions (birth, 4 months, yearly between 1–16 years, and then at 18, 21, 25, 30 and 35 years). 3. The Mater Hospital and University of Queensland Study of Pregnancy (MUSP) Between 1981 and 1983, 8556 consecutive pregnant women attending for their first clinic visit at average 18 weeks’ gestation at the Mater Misericordiae Mothers’ Hospital in Brisbane, Australia, were invited to participate in this longitudinal birth cohort (Keeping et al., 1989). Of those invited, 8458 (99%) agreed to participate in the study and 7223 gave birth to a live singleton child. The response proportion was 84%. Follow-up: The cohort has now been studied on eight occasions (18 weeks gestation, birth, 4, 5, 14, 21, 27 and 30 years). 4. The Personality and Total Health (PATH) Through Life Project (Anstey et al., 2012) is a longitudinal cohort study that commenced in 1999 with the recruitment of 7485 young (age 20–24 years at baseline, n = 2404), midlife (age 40–44 years at baseline, n = 2530) and older (age 60–64 years at baseline, n = 2551) adults randomly sampled from the electoral roll of the Australian Capital Territory and the nearby city of Queanbeyan, Australia. To align with the focus of the Consortium,
Australian & New Zealand Journal of Psychiatry, 49(4)
only the youngest PATH cohort was included in the CCRC. The response rate among those contacted who were in the target age range (20–24 years) and still resident in the area was 58.6%. Follow-up: The cohort has been assessed on four occasions between 1999 and 2011 (age 20– 24 years at baseline, 24–28 years, 28–32 years and 32–38 years). 5. The Victorian Adolescent Health Cohort Study (VAHCS) (Patton et al., 2007) is a longitudinal study of a representative sample of midsecondary school adolescents in Victoria, Australia. In 1992, participants were recruited via schools at the end of Year 9 (wave 1, mean age 14.9 years) or the start of Year 10 (wave 2, mean age 15.5 years). Of the invited sample of 2032 students, 1943 (96%) were assessed at least once during the first six waves. Follow-up: Participants were assessed at six-monthly intervals on four further occasions during adolescence: wave 3 (mean age 15.9 years) – wave 6 (mean age 17.4 years); with a further four follow-ups in young adulthood: wave 7 (mean age 20.7 years) – wave 10 (mean age 35 years). 6. The Western Australian Pregnancy Cohort (Raine) Study (Newnham et al., 1993; McNight et al., 2012) commenced in 1989. An estimated 3222 (Mountain, 2013, personal communication) pregnant women at around 18 weeks gestation from King Edward Memorial Hospital in Perth, Western Australia, were invited to participate. Of these, 2900 pregnant women were recruited. These pregnancies resulted in 2868 children recruited into the cohort. Follow-up: Information was collected from the mothers during pregnancy and parent/guardian at each follow up until the participants were 17 years of age. Participants were assessed at birth and at ages 1, 2, 3, 5, 8, 10, 14, 17, 20 and most recently 23 years of age.
319
Hutchinson et al.
How has the CCRC pooled data across cohorts? There are two major approaches to data pooling. The first, commonly used method, combines study level results meta-analytically. This approach is relatively straightforward to apply when there is a clear research hypothesis, the exposure and outcome variables are clearly and consistently specified within and across studies, and the measure of effect size can be easily interpreted (Curran and Hussong, 2009). However, because meta-analyses are based on combining estimates at the study level, rather than at the individual participant level, investigation is limited by low sample size and power. That is, often the unit of analysis in meta-analysis is the study, when more power can be derived if the unit of analysis is the study participant. The second approach to data pooling, pooling of participant-level data, is less commonly used because gaining access to study participant data is more complicated (ethically and practically) than combining available studylevel results. It is, however, ideally suited to investigations of low frequency exposures and/or outcomes such as rarer behaviours (e.g. regular drug use) or genetic variants. The approach involves developing common scales of measurement across cohorts (harmonisation) and pooling participant-level harmonised data from multiple cohorts to create a single (large) integrated dataset of participants rather than studies. Data harmonisation and pooling allows models to be fitted directly to participant-level data and, therefore, provides a number of advantages over study-level meta-analysis. The major advantages of data harmonisation are that it (Curran and Hussong, 2009): 1. provides increased sample size which enables more powerful examination of less frequent exposures and outcomes; 2. becomes feasible to explicitly model sources of between-study
heterogeneity at the level of the observed data; 3. improves the stability of model estimation and reduces the influence of outliers and study specific characteristics; and 4. provides greater precision, particularly for tests of interaction and subgroup analyses. The CCRC has now developed a harmonised data set with over 20 variables, across four cohorts (ATP, CHDS, MUSP, VAHCS) with more than 7000 respondents, with the purpose of further examining questions around frequent or dependent cannabis use that may not be answered by analyses of individual cohorts. Selected PATH data on cannabis and depression have also been harmonised within this data set (Horwood et al., 2012). We intend to harmonise relevant data from all six cohorts in the CCRC subject to continued funding support. Table 1 provides an overview of the key domains of interest for data harmonisation, the availability of these data across the four main cohorts harmonised to date (ATP, CHDS, MUSP, VAHCS), and where data are available, the measures used in each of these cohort studies. The annual attrition rate across the six cohorts was low; in the range of 0.5–3.9% per annum among the cohorts; the retained (contactable) sample rates at most recent follow-up in the range of 70–96%1. Although all studies showed evidence of some selection bias attributable to sample attrition, analysis of the implications of such biases for study findings have shown these to be minimal (Silins et al., 2014). Table 2 provides descriptive data on demographic and cannabis use characteristics in the harmonised sample.
What has it found? Key findings and publications Findings from the analysis of the integrated data improved knowledge of the relationships between cannabis
use, mental health, other substance use and social development in young people (Horwood et al., 2010, 2012; Silins et al., 2014). For example, the age of initiation of cannabis use was found to be a significant factor in educational attainment, with early use (i.e.
