DUE 7/24/19 8 P.M EST 400 words not including title and ref min 3 APA 2 ARTICLES ATTACHED In any epidemiological study, the design and methodology used should be appropriate for that study. Investiga

PU34CH02-Power ARI 12 February 2013 19:27 From Developmental Origins of Adult Disease to Life Course Research on Adult Disease and Aging: Insights from Birth Cohort Studies Chris Power, 1Diana Kuh, 2and Susan Morton 3 1MRC Center of Epidemiology for Child Health/Center for Pediatric Epidemiology & Biostatistics, University College London Institute of Child Health, London WC1N 1EH, United Kingdom; email: [email protected] 2MRC Unit for Lifelong Health and Ageing, London WC1B 5JU, United Kingdom3Centre for Longitudinal Research—He Ara ki Mua, University of Auckland Tamaki Campus, Glen Innes, Auckland 1743, New Zealand Annu. Rev. Public Health 2013. 34:7–28 TheAnnual Review of Public Healthis online at publhealth.annualreviews.org This article’s doi:

10.1146/annurev-publhealth-031912-114423 Copyrightc 2013 by Annual Reviews.

All rights reserved Keywords lifetime socioeconomic position, growth trajectories, cognitive and emotional function, adult health, life course conceptual frameworks, intergenerational inﬂuences Abstract Maturation of long-running birth cohort studies has fostered a life course approach to adult health, function, and disease and related to conceptual frameworks. Using broad concepts of human development including physical, cognitive, and emotional function, birth cohorts provide insights into the processes across the life course and between generations that link to adult outcomes. We discuss ﬁndings on the determinants and health consequences of lifetime trajectories of body size, cognitive and emotional function, and socioeconomic position.

Findings from the studies suggest that, for some adult health outcomes, explanations will be incomplete unless exposures and processes from across the life course are taken into account. New birth cohort studies are poised to delineate further the nature and timing of life course relationships in contemporary generations of children. 7Annu. Rev. Public Health 2013.34:7-28. Downloaded from www.annualreviews.org by 144.82.38.29 on 09/25/14. For personal use only. PU34CH02-Power ARI 12 February 2013 19:27 CVD:cardiovascular disease INTRODUCTION Research on the developmental origins of adult disease is broad in scope, embracing many scientiﬁc disciplines, exposures, and outcomes.

Such research recognizes the potential for inﬂuences on early-life development to lead to changes that impact on disease risk decades later in adulthood. This developmental perspective is long-established within some disciplines, for example, those concerned with emotional and cognitive function, but for other disciplines it is relatively recent. A major impetus to developmental origins research was provided in the early 1980s by a series of studies linking low birth weight, as a proxy for poor prenatal growth, to increased risk of chronic diseases in adulthood, including cardiovascular disease (CVD) and diabetes (the fetal origins hypothe- sis) (2). Earlier in the past century, investigators were interested in early environmental in- ﬂuences on the individual’s constitution that might affect later mortality risk (52). Nonethe- less, the fetal origins hypothesis represented a shift in emphasis for research on adult chronic disease, which had focused largely on adult lifestyles. Emerging research over the past three decades has led to a convergence of evidence on the wide-ranging effects of early environment and associated development for later health outcomes (50, 99) and to establishment of the International Society for Developmental Origins of Health and Disease in 2003.

Birth cohort studies established some decades ago have been well positioned to TRAJECTORIES “A trajectory provides a long-term view of one dimension of an in- dividual’s life over time. These may be social states (such as work, marriage, socioeconomic position), psychological states (such as depression) or physiological states (such as lung function).

Implicit is the idea of a normative trajectory around which in- dividuals deviate” (52). For health function, a trajectory may in- clude a period of gain to a peak followed by a period of decline (e.g., for lung function).investigate developmental origins hypotheses, and in turn, these hypotheses have provided a stimulus for the establishment of new cohorts.

There are now several birth and infancy cohorts in Britain, New Zealand, Finland, and elsewhere, including those established in the past few years and longer-running studies with follow-up in some instances of ﬁve or more decades. Basic details of some of these studies are given inTable 1; the list is far from comprehensive but illustrates the range of birth dates, length of follow-up, and original study purposes. Both younger and older studies alike have tended to collect information on parental characteristics and social and family back- ground, as well as conditions during pregnancy and in early childhood. In addition, many older studies have assessed the physical, cognitive, behavioral, and emotional development of their participants and collected information on social destinations, lifestyles, and other putative inﬂuences on later disease risk. In recognition of the importance of charting early develop- mental milestones and trajectories (see sidebar on Trajectories), some younger cohorts have been instigated during pregnancy rather than at or soon after birth and also have more frequent contacts early in childhood than did some older studies. Longer-running studies have been able to investigate inﬂuences across developmental domains (physical, cognitive, emotional) in relation to health in later life, and with infor- mation collected at different life stages, these maturing birth cohorts have fostered a life course approach to adult disease. The objective of a life course approach in epidemiology is to establish how social and biological factors operating at different stages of life and across generations contribute to the development of adult health and disease over time (52). With its consideration of different ages, life course research seeks to understand inﬂuences of early-life exposures and development on later disease outcome and the processes occurring in the intervening years of life that link them.

Thus, life course epidemiology extends the de- velopmental origins of adult disease perspective by focusing attention on potentially sensitive 8Power ·Kuh ·MortonAnnu. Rev. Public Health 2013.34:7-28. Downloaded from www.annualreviews.org by 144.82.38.29 on 09/25/14. For personal use only. PU34CH02-Power ARI 12 February 2013 19:27 Table 1 Selected birth (or infancy) cohorts a Cohort name/country Year of birth Selection/design Nat baseline Follow-ups (timing) Nat last sweep (%) Initial reason for/focus of study National Survey of Health and Devel- opment/Britain (54, 108) 1946 Socially stratiﬁed sample of singleton babies born in one week, March 1946, to married women 5,362 Birth, 2, 4, 6, 7, 8, 9, 10, 11, 13, 15, 19, 20, 22, 23, 26, 31, 36, 43, 47(F), 48(F), 49(F), 50(F), 51(F), 52(F), 53, 54(F), 60–64 years 2,661 To consider cost of and care in pregnancy and childbirth. Social class differences in maternal and child mortality and morbidity National Child Development Study/Britain (77) 1958 Allborninone week, March 1958 17,638 Birth, 7, 11, 16, 23, 33, 42, 45, 46, 50 years 9,790 To identify social and obstetric factors linked to stillbirth and neonatal death Aberdeen Children of the 1950s Study/Britain (62) 1950– 1956 All primary-school children in Aberdeen in 1962 12,150 Perinatal b(linked), 7, 9, 11, 45–50 years 7,655 To understand predictors of low childhood cognition Northern Finnish Birth Cohort Study/Finland (87) 1966 Live born, European descent, with expected birth dates in 1966, Oulu and Lapland (northern Finland) 12,058 Pregnancy, birth, 1year,14years, 31 years 8,690 To examine risk factors for childhood mortality and morbidity in a geographically deﬁned population 1970 British Birth Cohort Study/Britain (20) 1970 Allborninone week, April 1970 16,571 Birth, 5, 10, 16, 26, 30, 34, 42 years 9,656 at 34 years To examine the social and biological characteristics of mothers in relation to neonatal morbidity Dunedin Multidisciplinary Health and Development Study/New Zealand (98) 1972 All births in Dunedin March 1972–April 1973 enrolled at 3 years 1,037 3, 5, 7, 9, 11, 13, 15, 18, 21, 26, 32 years 972 To conduct a longitudinal population-based multidisciplinary study of child health, development, and behavior Christchurch Health and Development Study/New Zealand (24) 1977 Children born April–early August 1977 in Christchurch 1,265 Annually from birth to 16, 18, 21, 25, 30 years 934 To conduct a longitudinal birth cohort focused on child health and development Avon Longitudinal Study of Parents and Children (ALSPAC)/England (7) 1991– 1992 Offspring of all pregnant women in the county of Avon, estimated delivery date April 1, 1991—December 31, 1992 15,247 eligible preg- nancies en- rolled 68 time points, birth to 18 years 7,729 c To investigate modiﬁable inﬂuences on child health and development (Continued) www.annualreviews.org •Life Course Research on Adult Disease 9Annu. Rev. Public Health 2013.34:7-28. Downloaded from www.annualreviews.org by 144.82.38.29 on 09/25/14. For personal use only. PU34CH02-Power ARI 12 February 2013 19:27 Table 1 (Continued) Cohort name/country Year of birth Selection/design Nat baseline Follow-ups (timing) Nat last sweep (%) Initial reason for/focus of study Southampton Women’s Survey/England (44) 1998– 2007 Births to recruited 12,583 prepregnant women 3,156 Prepregnancy, pregnancy (multiple), 6 months, 1, 2, 3, 4, 6, 8 years 1,477 at 6years To understand perinatal and early life determinants of children’s growth and development Millennium Cohort Study (MCS)/United Kingdom (14) 2001 Nationally representative sample across United Kingdom recruited during infancy 19,244 9 months, 3, 5, 7, 11 years 15,590 at 7years To understand the biological and environmental determinants of contemporary child development aNote: This list of studies is not exhaustive. Birth cohorts documented have collected perinatal information and have existed for long enough to provide sufﬁcient longitudinal data to inform the areas addressed in this article. The success of older cohorts has provided the impetus for many new birth cohort studies begun around or after the turn of the millennium. Newer studies include those in the European Child Cohort Network (EUCCONET), as well as studies in Australia (Growing Up in Australia—LSAC) and New Zealand (Paciﬁc Island Families Study and Growing Up in New Zealand). EUCCONET includes the Norwegian Birth cohort (MoBa), Danish National Birth cohort, Generation R Study (The Netherlands), Born in Bradford (United Kingdom), Millennium Cohort study (MCS), Growing Up in Scotland (GUS), Growing Up in Ireland (GUI), ELFE (France) and will include a newly planned UK study.

bThe Aberdeen Children of the 1950s Study is included here because it has birth information for the 14,932 children who were part of this cohort.

However, they were enrolled between ages 5 and 11 years (in 1962), and perinatal data were linked for the cohort at that time.

cCompleting≥1 item during the transition to adulthood. periods in childhood and adolescence as well as in the prenatal period. It extends the adult lifestyle theories of chronic disease by focusing attention on the early acquisition of lifestyle and its cumulative effects. It extends social causation theories of adult chronic disease by drawing attention to the impact of the socioeconomic environment in childhood as well as adulthood.

It also extends both developmental origins and adult theories of disease causation by consider- ing the joint action of early and later exposures.

With an increasing number of studies and duration of follow-up, birth cohorts are now charting new territories. Some recently estab- lished cohorts are focusing on understanding early development in the wider neighborhood and societal context (69). Older birth cohort studies, often with decades of information, are examining inﬂuences on the population range of functional capacities and disease risk into later adulthood, and an agenda on life course inﬂuences on aging is already emerging. Many birth cohorts now incorporate genetic factorsand are contributing to the discovery of genetic variants associated with important phenotypes (e.g., obesity) through consortia for genome- wide association studies. Moreover, the cohorts offer the prospect of advancing understanding of (epi)genetic inﬂuences on developmental tra- jectories and how these relate to the potentially modiﬁable environmental context.

At a time when birth cohort studies are evolving to address a range of social, economic, and health questions, it is timely to critically review their contributions and identify future challenges. This article does not purport to provide a comprehensive summary of the vast literature from these cohorts, but it consid- ers some main themes of relevance to public health. Speciﬁcally, we consider examples of where birth cohorts have stimulated thinking about the life course frameworks that might guide research on pathways to adult disease and where, in this regard, they have added new knowledge. Evidence from the studies has been informative on many important themes, 10 Power ·Kuh ·MortonAnnu. Rev. Public Health 2013.34:7-28. Downloaded from www.annualreviews.org by 144.82.38.29 on 09/25/14. For personal use only. PU34CH02-Power ARI 12 February 2013 19:27 SEP:socioeconomic position e.g., on the natural history of some condi- tions, long-term outcomes of speciﬁc preg- nancy exposures/characteristics, and determi- nants of healthy lifestyles, but we do not cover these themes in any detail here. Instead, we summarize some of the work on the follow- ing in relation to adult function and disease:

lifetime socioeconomic position (SEP), lifetime growth trajectories, and cognitive and emo- tional development at different life stages. For each of these research areas the birth cohorts have contributed a substantial body of empir- ical evidence. With the exception of lifetime SEP, which to some extent can be investigated retrospectively, associations for growth, cogni- tion, and mental health can be assessed only with prospectively obtained measures at differ- ent life stages.

CONCEPTUAL FRAMEWORKS Conceptual frameworks have been developed to guide research on the life course processes leading to adult disease. Investigators have pro- posed various general models that have then been adapted and applied to different health outcomes. Although such frameworks have been used in other contexts (e.g., record link- age studies), they are particularly relevant in the investigation of birth cohorts.

Figure 1provides an example of a general framework, with main components that are often considered in life course research. This simpliﬁed representation incorporates inter- generational factors, developmental domains (cognitive, emotional, and physical), social identities and health behaviors, and environ- mental inﬂuences that potentially act at all life stages to affect later health.Figure 1is pre- sented to highlight ﬁve main points. First, birth cohort studies, in general, can focus on deter- minants of the full spectrum of both health and disease in a population. Second, life course tra- jectories for body functions (e.g., muscle func- tion, lung function) are a dynamic way to study lifetime inﬂuences on health and disease; these trajectories capture the natural history of bio- logical systems that grow and develop rapidly LIFE COURSE MODELS OF ADULT DISEASE OUTCOMES The critical period model (also called biological programming or latency model) refers to exposures acting during a critical window of development that affect the structure or function of organs, tissues, or body systems and which, in turn, affect later disease risk. This model underpins the fetal origins of adult disease hy- pothesis. Sensitive-period models are similar, with exposures ex- erting greatest effects during times of rapid development, but there is greater scope for modiﬁcation by other inﬂuences than there is with a critical period model.

The accumulation of risk model refers to the adverse effect on later disease of exposures accumulating over the life course.

It thereby focuses on total burden of insults, i.e., the number, duration, or severity of a range of health-damaging environmen- tal, socioeconomic, and behavioral factors.

The chains of risk model (also pathways model) refers to se- quences of events or exposures, whereby one exposure increases the likelihood that another will follow, leading to a ﬁnal expo- sure(s) that is causally related to later disease. Links are not de- terministic, and earlier exposures do not affect disease risk but often lead to a ﬁnal link in the chain that does affect later disease risk. Social, biological, and psychological factors can be part of chains of risk models, possibly acting as mediating or modifying factors.

Sources: adapted from Hertzman et al. (38), Keating & Hertzman (50), Ben-Shlomo & Kuh (4), and Kuh et al. (52) during the prenatal, prepubertal, and pubertal periods, reaching a peak or plateau at maturity and gradually declining with age (Figure 2).

The progressive, generalized deterioration in function postmaturity can be thought of as bi- ologically aging; the generally accepted dispos- able soma theory of aging suggests this is caused by increasing molecular and cellular damage from environmental insults and chance (51).

Third, inﬂuences over the life course might operate in several ways to affect adult function and disease. Researchers have identiﬁed models for the alternative processes that might be in- volved; main models including critical period, accumulation, or chains of risk are shown in the sidebar, Life Course Models of Adult Disease Outcomes. Although these life course models www.annualreviews.org •Life Course Research on Adult Disease 11Annu. Rev. Public Health 2013.34:7-28. Downloaded from www.annualreviews.org by 144.82.38.29 on 09/25/14. For personal use only. PU34CH02-Power ARI 12 February 2013 19:27 Intergenerational influences Cognitive function Childhood Adulthood Physical health Emotional health Environmental influences Emotional health Physical health Adult outcome Cognitive function/ education Parents/ grandparents Social (origins) identity/ health behaviorSocial (destinations) identity/health behaviorHealth decline Health gain Figure 1 Simpliﬁed framework linking early-life exposures with adult outcome.

can be seen as distinct, they are not necessarily mutually exclusive, and one can envisage varia- tions of basic models. For example, an exposure that operates during a critical or sensitive pe- riod may also accumulate with other exposures over the life course, possibly through modiﬁca- tion of earlier factors by those occurring later in life. Fourth, developmental domains may Years of life Level of function 0 1020304050607080901000 10 20 30 40 50 Level below which limitations may occur Adult risk factors Early life risk factors A B C D Functional/structural reserve Figure 2 Life course functional trajectories. LineA, normal development and decline; lineB, exposure during development reducing functional reserve at maturity; lineC, exposure acting postmaturity, accelerating age-related decline; lineD, combination ofBandC. Figure modiﬁed fromA Life-Course Approach to Chronic Disease Epidemiology,edited by Diana Kuh and Yoav Ben-Shlomo, 2nd edition, 2004, chapter 1, page 9, ﬁgure 1.2, by permission of Oxford University Press. coevolve, e.g., between physical developmental characteristics such as height and emotional or cognitive development, whereas during adulthood, relationships between health and other factors (e.g., social circumstances, health behaviors and so forth) may be bidirectional.

Hence, there are numerous links across the domains represented inFigure 1; birth cohort studies provide several examples of coevolution and bidirectional relationships. Fifth, develop- mental trajectories and subsequent health in adulthood can be affected by intergenerational inﬂuences. These intergenerational inﬂuences likely represent the effect of both genetic and environmental inﬂuences acting over time (12).

Although not speciﬁed in the simpliﬁed Figure 1, environmental inﬂuences can operate at many levels, including individual (micro), community and neighborhood (meso), or national and international (macro) levels (38). For example, smoking in a population can be inﬂuenced at the individual level in terms of behaviors relating to smoking initiation and maintenance (addiction), at the community level in terms of peer relationships and behavior as well as access and availability of tobacco, and also at the national level in terms of taxation or smoke-free legislation to prohibit smoking 12 Power ·Kuh ·MortonAnnu. Rev. Public Health 2013.34:7-28. Downloaded from www.annualreviews.org by 144.82.38.29 on 09/25/14. For personal use only. PU34CH02-Power ARI 12 February 2013 19:27 Grandparent Parent Offspring Time Household level Neighborhood level National level Joint neighborhood effects Childhood cohort effect Period effect acting on all three generations A B C Figure 3 Multigenerational schema: inﬂuences of hierarchical and life course exposures on disease risk across three related individuals. Intergenerational links, which can be common genetic and/or social inﬂuences, are shown between grandparents, parents, and offspring. Life course links are represented by exposures occurring at different life stages and at differing (household, neighborhood, and national) levels.

Ben-Shlomo & Kuh (4) illustrate inﬂuences acting across time and across individuals with the example of adverse neighborhood conditions affecting a mother and her child (A), and with national exposures (e.g., war-time rationing) as either speciﬁc to a single population cohort (B) or experienced by all individuals (C).

Modiﬁed from Ben-Shlomo Y, Kuh D, 2002, “A life course approach to chronic disease epidemiology:

conceptual models, empirical challenges and inter-disciplinary perspectives,”Int. J. Epidemiol.31(2):285–93, by permission of Oxford University Press (4). Adapted from Hertzman et al. (38). BMI:body mass index indoors. Inﬂuences acting at these different levels might affect successive generations at different points in their lives, with consequent variations in impact: An exposure could occur at a critical or sensitive period for one genera- tion with attendant effects over the life course, but with lesser impact when experienced at a later life stage for another generation.Figure 3 illustrates the links between generations and life stages, with inﬂuences from different levels (individual, neighborhood, and national) of the broader social environment, as successive gen- erations live through different periods of time.

The general frameworks inFigures 1and 3, and other such schemas (29, 69), are not intended to be comprehensive, and when they are adapted and reﬁned to investigate particularlife course relationships, details of lifetime exposures and outcomes can be elaborated.

For example, a study of body mass index (BMI) at different life stages considered exposures from the prenatal period (maternal age, BMI, blood pressure, and smoking in pregnancy), adolescence, and adulthood (physical activity, diet, smoking, alcohol consumption, and SEP) and found that life course factors (e.g., physical activity) had strong associations, separate from genetic factors, with BMI at age 31 years (49).

In view of the complexity of elaborated models, many initial life course studies focus on SEP at different life stages, which, although a broad, nonspeciﬁc, and distal measure, has been used to indicate when related exposures might be operating. www.annualreviews.org •Life Course Research on Adult Disease 13Annu. Rev. Public Health 2013.34:7-28. Downloaded from www.annualreviews.org by 144.82.38.29 on 09/25/14. For personal use only. PU34CH02-Power ARI 12 February 2013 19:27 LIFETIME SOCIOECONOMIC POSITION AND ADULT DISEASE SEP is a long-established determinant of health and disease, whereby better outcomes are gen- erally seen with increasing socioeconomic ad- vantage; i.e., generally, the association is graded with health beneﬁts for each increase in SEP.

From their earliest days, many birth cohort studies have had a particular interest in SEP, and they have shown a typical trend whereby Hearing at 4 kHzIgE FEV1 Fibrinogen Triglycerides HDL cholesterol Total cholesterolHbA1cBMI DBPSBP Disease risk factor –0.10 –0.05 0 0.05 0.10 Difference (SD score) per increase in social class Figure 4 Associations of child and adult social class with disease risk factors at age 45 years in the 1958 British birth cohort. Disease risk factors at 45 years include systolic and diastolic blood pressure (SBP and DBP), body mass index (BMI), HbA1c, total and HDL cholesterol, triglycerides, ﬁbrinogen, one-second forced expiratory volume (FEV1), total immunoglobulin E (IgE), hearing threshold at 4 kHz. Risk factors are converted to standard deviation (SD) scores to allow comparison of associations for child and adult social class across different outcomes. Estimated effects are differences per unit increase in social class (on a six-point scale from professional to unskilled manual) adjusted for sex and mutually adjusted for child and adult class. Filled circles [95% conﬁdence interval (CI)], child social class; unﬁlled circles (95% CI), adult social class. From Power et al. (76). exposures cluster by the individual’s or family of origin’s SEP. In different countries and at different times, birth cohorts have demon- strated that SEP associations are evident at all life stages with indicators of prenatal and postnatal development and reproductive, functional, and disease biomarkers as well as morbidity and mortality (5, 20, 41, 42, 75, 77, 108).

To provide clues about whether exposures in early as well as later life have long-term effects, birth cohorts have been used to es- tablish whether childhood SEP is associated with later health, independent of adult SEP.

Childhood SEP has predicted many (but not all) later outcomes, including poor cardiores- piratory ﬁtness and dental health at 26 years (75), self-rated health at 33 years (83), liter- acy and numeracy at 42–43 years (90), obe- sity (75, 76, 78), CVD risk factors [blood pressure, inﬂammatory and endothelial mark- ers (75, 76, 103), metabolic syndrome (59)], hearing thresholds (17), chronic widespread pain (76), IGF1 (insulin-like growth factor) at 45 years (56), cognitive (91) and physical func- tion at 53 years (102) and older ages (5), tim- ing of menopause (33), and premature mortality (55).Figure 4illustrates the independent asso- ciations for child and adult SEP with several but not all disease risk factors in midlife; it suggests that for a range of biomarkers the estimated as- sociations for childhood are often as strong as those for adulthood SEP. In addition, birth co- hort studies suggest that SEP associates with re- productive outcomes and offspring birth weight across generations. Speciﬁcally, they suggest that a mother’s own growth in early life, which is inﬂuenced by her parents’ SEP, inﬂuences her ability to nourish her offspring (12, 68, 70).

Findings for these multiple health and disease indicators add to evidence from a wider range of studies, e.g., with retrospective recall of child- hood SEP, showing associations with mortality from speciﬁc causes, such as CVD.

We can identify several points of interest.

First, the observation that associations of childhood SEP with functional measures or biomarkers (intermediate phenotypes) are 14 Power ·Kuh ·MortonAnnu. Rev. Public Health 2013.34:7-28. Downloaded from www.annualreviews.org by 144.82.38.29 on 09/25/14. For personal use only. PU34CH02-Power ARI 12 February 2013 19:27 evident well before clinical manifestation of disease serves to highlight the role of earlier life inﬂuences. Many outcomes are related to early- life SEP, but some are not, providing speciﬁcity for those adult conditions for which there may be a contribution of early-life exposures.

Second, the fact that associations for childhood SEP mentioned above are independent of adult SEP suggests that an accumulation of exposures at different life stages may be operating (accu- mulation model). Reinforcing this point, effects on adult health have been observed of SEP accumulation across multiple ages (83). Such ﬁndings do not preclude critical or sensitive- period effects, or variations in importance of inﬂuences at different life stages for different outcomes. Third, many studies of childhood SEP were from generations who experienced considerable hardship by today’s standards, and we might expect that with increasing afﬂuence, socioeconomic adversity in childhood had less- ened to the point that it is no longer relevant to adult health. Yet, later-born cohorts, such as the Dunedin (New Zealand) study, provide lit- tle support for this expectation, given ﬁndings that poorer SEP in childhood had a detrimental effect on the cardiorespiratory ﬁtness and den- tal health of 26-year-olds born in 1972–1973, irrespective of their adult SEP (75). Such obser- vations imply that, even in a developed modern society, the socioeconomic conditions in which children are born and raised inﬂuence their health in adult life. Fourth, ﬁndings from birth cohort studies have stimulated the development of theory on how childhood disadvantage leads to impaired later health outcomes (e.g., as in the models outlined in the sidebar, Life Course Models of Adult Disease Outcomes); they have provided empirical evidence on whether and when alternative models apply and, in addition, have been used to inform policy development (see below).

Empirical evidence is available to suggest the processes through which childhood disadvan- tage leads to poorer health outcomes. For exam- ple, in one study, links between childhood SEP and midlife physical performance were partially mediated by growth, development, childhoodhome environment, and adult SEP (102). In a second study, childhood SEP was associated with all-cause mortality among women (though not men): Women from manual origins had about double the risk of death between 26 and 60 years compared with women from nonman- ual origins (55). The study tested the hypothesis that childhood SEP inﬂuences adult mortality via cognitive development and its subsequent link to health behavior (speciﬁcally adult smok- ing). Associations for child SEP remained, al- though attenuated after allowing for mediation by cognition and smoking, and were in addition to those seen for adult SEP (55). Thus, part of the explanation for a link between childhood SEP and adult mortality appeared to be due to the inﬂuence of childhood SEP on cognitive de- velopment and subsequent lifestyles, but with a role for additional processes. Further opportu- nities exist to unravel the processes linking child SEP to later outcomes. Of relevance here are recent epigenetic studies, for example, showing associations of SEP with methylation changes in blood samples taken in mid-adulthood, sug- gesting that SEP-related factors contribute to general DNA methylation variation (6). Both child and adult SEP had distinct epigenetic signatures (indicated by differentially methy- lated promoters); i.e., there was little overlap of their signatures. This ﬁnding opens up the prospect that early-life exposures change gene expression, possibly programming genes crit- ical for human health, such as those involved in cardiovascular and behavioral pathologies (6).

In addition to providing clues on the timing of inﬂuences on later health outcomes, research on the birth cohorts has improved our under- standing of the origins of social inequalities in health. As mentioned above, the cohorts demonstrate social inequalities from early- to later-life stages, across a range of health indicators (5, 20, 41, 42, 75, 77, 108). They also provide some evidence for a link between health status and social mobility: Poor health is associated with downward mobility, and better health is associated with upward mobility (28, 75). Yet, in general, evidence from the birth www.annualreviews.org •Life Course Research on Adult Disease 15Annu. Rev. Public Health 2013.34:7-28. Downloaded from www.annualreviews.org by 144.82.38.29 on 09/25/14. For personal use only. PU34CH02-Power ARI 12 February 2013 19:27 cohorts suggests that health-related social mobility is not a major explanation for social inequalities in adult health (75, 77, 103).

LIFETIME GROWTH TRAJECTORIES AND ADULT DISEASE The fetal origins hypothesis stimulated much controversy on whether the prenatal or postna- tal period was most inﬂuential for adult chronic disease. The growth acceleration hypothesis was subsequently proposed, which argued for detrimental effects of rapid postnatal growth on later disease risk (99). Such perspectives have fostered an appreciation of the early timing of some inﬂuences on adult disease and have directed attention toward a range of poten- tially relevant anthropometric measurements, including birth weight, height, and BMI.

With serial measurements of body size onward from birth, as well as information on adult outcomes, the older birth cohort studies show that growth trajectories over decades of life are associated with particular adult outcomes. In addition, against a backdrop of secular trends in height and adiposity, both younger and older cohorts have had a major focus on the putative inﬂuences at different life stages on the anthro- pometric (e.g., low birth weight or obesity) characteristics of their populations.

Cohort studies show the well-established associations between body size (low birth weight, shorter height, and higher BMI) and unfavorable CVD risk factors, such as blood pressure and lipids, at different life stages. As expected, current size in adulthood has been of paramount importance for many CVD risk factors. More uniquely, ﬁndings from the older cohorts suggest that the growth trajectory leading to adult height, weight, and BMI is highly relevant to adult disease risk.

Starting with growth early in life, several cohorts show that low birth weight is linked to elevated blood pressure (35, 45, 64), lipids (10, 100), and glucose metabolism (104) in adulthood. Birth cohorts have contributed to systematic reviews that aim to establishthe consistency and overall magnitude of associations, for example, of lower birth weight or accelerated postnatal growth with adult out- comes such as elevated blood pressure or type 2 diabetes (43, 109). In some instances the mag- nitude of effect is modest; nonetheless, ﬁndings are consistent with the fetal origins hypothesis.

Support for a growth acceleration effect is available also. For example, faster weight gain in infancy predicts childhood obesity (16), body fat, timing of menarche (71), and higher adult blood pressure (45), and factors associated with catch-up growth in infancy (such as maternal smoking in pregnancy) are associated with elevated risk of adult obesity (45, 79). The birth cohorts show that it is not just weight gain in infancy that is detrimentally associated with adult CVD risk factors (e.g., blood pressure) but also excessive gain across life stages from child, adolescence, and adulthood (35, 39, 64, 112). Moreover, individuals who are thinner in childhood/adolescence appear to be more vulnerable to the effects of gains in BMI for blood pressure (64) and lipids (74). Excessive BMI gain is also adversely associated with glucose metabolism; however, in this instance, effects are stronger for those who were heavier rather than thinner in childhood, consistent with the ﬁnding that longer duration of obesity adversely affects risk of type 2 diabetes (86).

This latter ﬁnding for longer duration (or earlier onset) of obesity seems to be due largely to the greater adiposity in adulthood of those with earlier onset. Indeed, the birth cohorts show strong tracking of obesity from child to adulthood (82, 111).

Birth cohorts are experiencing the obesity epidemic at different life stages. A comparison of two UK cohorts showed major shifts in prevalence of overweight and obesity, even for generations born just 12 years apart (1946 and 1958 cohorts) (63). Differing trajectories for these populations are evident, with steeper gains in BMI and longer duration of overweight or obesity for the younger generation (63).

Such differences in trajectories of adiposity are likely to have implications for disease burden in adulthood. Several studies have therefore been 16 Power ·Kuh ·MortonAnnu. Rev. Public Health 2013.34:7-28. Downloaded from www.annualreviews.org by 144.82.38.29 on 09/25/14. For personal use only. PU34CH02-Power ARI 12 February 2013 19:27 concerned to identify exposures associated with hazardous BMI trajectories (i.e., rapid BMI gain or young age of obesity onset).

Notably, across several cohorts, less advantaged childhood SEP has been associated with more rapid gain in BMI at different life stages: within childhood (42), from child to adulthood (84, 106), and within adulthood (34). The higher BMI in childhood of those with less advantaged SEP appears to have become more pronounced in younger compared with older generations, suggesting that there is a widening social gap (73).

Several other factors have been linked to rapid BMI gain or high BMI in childhood, in- cluding high parental BMI, maternal smoking in pregnancy, lone parenthood, others smoking in the same room as the child, more frequent television viewing, and short sleep duration (30, 88). Some factors continue to show associations with BMI at later-life stages. For example, frequent television viewing and low activity levels in adolescence and early adulthood are associated with greater central adiposity, BMI, or gains in BMI through to midlife (32, 72, 106), while fast-food consumption and dieting to lose weight also show associations with faster BMI gain (106). Earlier timing of puberty (58, 82) and genetic factors have also been linked to higher BMI or BMI gain at later-life stages. Us- ing the growing knowledge of genetic variants associated with adiposity, one study showed that factors at different life stages, such as physi- cal activity, had strong associations with BMI at 31 years independent of adiposity-related gene (FTO) variants (49). The association of FTO with adult BMI appeared to operate in part via its effects on earlier BMI development. Simi- larly, a risk score of several adiposity-related gene variants was associated with weight gain in infancy and throughout childhood to 11 years (19), and when investigators considered weight gain over a longer period of the life course, the combined effect of adult obesity susceptibility variants was conﬁned to childhood (18). This latter ﬁnding is consistent with earlier research on parent-offspring associations showing that parental obesity has a more limited effect onoffspring obesity after age 10 years than it does in earlier childhood (110). Intergenerational associations for adiposity are well established; offspring of obese parents have an elevated risk of overweight/obesity. Lifestyle and sociodemographic factors for offspring did not explain parent-offspring BMI associa- tions; however, Power et al. (85) have shown variations in the intergenerational association over successive generations and also by social class, which suggests that intergenerational transmission of adiposity at a population level is not immutable. Intergenerational transmission of adiposity may be due in part to greater nutrient transfer from obese mothers to the fetus, leading to permanent changes in appetite, metabolism, and other functions, as envisaged by the fetal origins hypothesis. The observation that high maternal weight gain in early pregnancy increases the risk of offspring obesity in adolescence (57) is consistent with such prenatal effects. However, others have argued that if there were a speciﬁc maternal effect, through intrauterine programming, the maternal-offspring adiposity association would be stronger than the paternal-offspring association; yet differential associations have not been found (11). Although such observa- tions do not discount intrauterine effects due to maternal characteristics or exposures, they do shift attention to postnatal inﬂuences on adiposity or at least to the interaction of pre- and postnatal conditions.

Intergenerational associations have also been documented for size at birth: Offspring’s birth weight increases in relation to increases in the mother’s own birth weight (21) or height (1). Intergenerational studies, usually reliant on record linkage with birth cohort data, have additionally demonstrated the inﬂuence of maternal lifetime anthropometry on her offspring’s size at birth (70). Comparing across a normal population distribution, mothers who were large at birth themselves, and who grew most rapidly in early childhood, tended to have the largest babies themselves. Moreover, rapid growth after early childhood further increased the likelihood of mothers delivering heavier www.annualreviews.org •Life Course Research on Adult Disease 17Annu. Rev. Public Health 2013.34:7-28. Downloaded from www.annualreviews.org by 144.82.38.29 on 09/25/14. For personal use only. PU34CH02-Power ARI 12 February 2013 19:27 infants (13, 67). One birth cohort study showed maternal but not paternal birth weight and weight gain in early childhood to be positively associated with next-generation birth weight (40). These data suggest that the capacity to nourish infants in utero is inﬂuenced by a mother’s own growth throughout her life, rather than solely by her adult size, current behaviors, and health status (67). Other multi- generational studies of size at birth suggest that the correlations in size at birth between parents and their children while being determined in part by shared genes are also considerably inﬂuenced by shared environments across generations (12). Among other inﬂuences associated with impaired prenatal growth, some (e.g., maternal smoking in pregnancy) observed decades ago in older cohorts are seen still in more recent studies.

Key themes can be identiﬁed from the grow- ing birth cohort literature on intergenerational and lifetime relationships with anthropometric characteristics, for which the above provides only a partial account. First, with regard to the health burden associated with adiposity, many studies highlight the importance of current BMI for adult CVD risk but emphasize also the importance of gain in BMI throughout the life course. They also suggest that there are additional associations for prenatal body size, as indicated by birth weight. The cohort studies suggest that for particular CVD biomarkers how individuals arrive at the BMI they have in adulthood, i.e., their BMI trajectories, matters.

Second, factors related to the development of adiposity are observed across different life stages, including three periods suggested as being particularly inﬂuential for the develop- ment of obesity: the prenatal period, the period of adiposity rebound (at about 5–7 years), and puberty (15). Many of the inﬂuences identiﬁed are modiﬁable. Third, the intergenerational relationships observed for birth weight, height, and adiposity suggest that, in some instances, causes and health consequences associated with these physical characteristics are more than lifelong and extend across previous and subsequent generations. COGNITIVE AND EMOTIONAL DEVELOPMENT AT DIFFERENT LIFE STAGES AND ADULT DISEASE Cognitive and emotional development are key domains for which long-term associations with adult health have been documented in birth co- hort studies; moreover, these studies have also offered insights on the intervening life course pathways (Figure 1). Continuities between child development and function in adult life are strong: Poor cognitive function in childhood predicts poor educational qualiﬁcation and literacy and numeracy levels in adulthood (90, 97) and faster decline in memory, speed, and concentration in midlife (92); in addition, for emotional development, childhood psycholog- ical problems are associated with increased risk of anxiety and affective disorder in adulthood (9, 23). Cognitive and emotional development also inﬂuence adult SEP (28, 90) and are asso- ciated with several adult health outcomes. For example, cognition in childhood predicts self- rated health at 33 years (65); long-term sickness absence (unable to work) in adulthood (37), obesity and weight gain (8), and other biomark- ers for CVD (blood pressure, lipids, and glucose metabolism) at 45 years (80); hyposecretion of postwaking cortisol at 45 years (26); and timing of menopause (66), physical performance (53), and premature mortality (55). In general, those with poor cognitive scores in childhood have the least favorable outcomes, in some instances even when studies control for family back- ground and other relevant factors. Likewise, worse emotional status in childhood predicts adult outcomes, such as obesity at 26 years (93), poor self-rated health at 33 years (65), sickness or disability at 46–51 years (36), injury risk through to 42 years (48), and premature mortality (47). Several studies to date suggest that health behavior and adult social destina- tions are important mediators in associations of childhood cognition or emotional status with adult health (37, 80). However, for some outcomes, such as timing of menopause, phys- ical performance, and psychological disorders, 18 Power ·Kuh ·MortonAnnu. Rev. Public Health 2013.34:7-28. Downloaded from www.annualreviews.org by 144.82.38.29 on 09/25/14. For personal use only. PU34CH02-Power ARI 12 February 2013 19:27 effects of childhood cognition are not mediated by social and behavioral risks and may instead reﬂect common CNS pathways.

Identiﬁcation of adult health associations for cognitive and emotional development underscores the importance of understanding the determinants of these child developmental domains, a valuable goal in its own right.

However, if impaired cognitive and emotional development generate adverse trajectories for adult health outcomes, then additional questions are raised, such as whether there are common early-life causes of these child developmental domains and adult health outcomes. Evidence on the determinants of cognitive and emotional development from the birth cohorts spans parental characteristics as well as prenatal, early postnatal, and later childhood/adolescent environments.

Links with lower childhood cognitive ability have been observed for less advantaged social classes around the time of birth, as have younger maternal age and high parity, poor maternal physical condition, birth of the child outside of marriage, prematurity, poor intrauterine growth, short childhood height, parenting style, and less stimulating home learning environment (46, 61, 81, 89, 96, 97, 101). The birth cohort literature suggests that socioeconomic environment has particularly strong associations; other factors such as birth weight, have had a lesser impact.

Moreover, although the socioeconomic envi- ronment in early life appears to be important in shaping cognitive development, additional contributions are evident for accumulated disadvantage (or poverty) over different ages in childhood/adolescence (96). Greater beneﬁts to childhood cognitive ability of an enriched home/learning environment, as indicated by more frequent parental reading to the child and interest in education, are seen among the least advantaged socioeconomic groups (81).

Adversities in the social environment during childhood have also been associated with emo- tional development and adult mental health, including being reared in an institution, physi- cal and sexual abuse, parental psychopathologyor substance abuse, family conﬂict, parental divorce, childhood neglect, and other facets of parental style (22, 23, 94). Of particular note is the accumulation of such factors, both during childhood and also from childhood to adult- hood. Pathways between childhood stressors and adult mental ill-health have been identiﬁed including cumulative adversity, or additive strain models, inﬂuences on vulnerability via the development of self-esteem, mastery and other psychological attributes, and inﬂuences on the individual’s social trajectory, e.g., future employment/education opportunities, social support, and life events (94).

Three main points are highlighted here from birth cohort study ﬁndings on cogni- tive/emotional development and later health.

First, for both cognitive and emotional devel- opment, strong continuities into adulthood and links with adult lifestyles and social destinations (mentioned here in brief) (3, 28, 90) provide pathways from development in childhood to adult health and disease. For cognitive develop- ment, some evidence suggests that part of the explanation for associations with later health outcomes may be due to common early-life causes, as indicated for example by social class at birth or by birth weight, although common causes do not appear to play a major role. By comparison, studies examining life course rela- tionships suggest that adult lifestyles and social destinations are important intermediaries, accounting for much of the associations with later health. Such ﬁndings may be interpreted as indicating the primacy of chains of risk or accumulation models (see sidebar, Life Course Models of Adult Disease Outcomes) underlying cognitive ability (or emotional development) and later health relationships.

However, given current knowledge, critical- or sensitive-period (latent) effects cannot be excluded. Second, ﬁndings from the birth cohorts tend to support experimental and other evidence suggesting that early life is a sensitive period for cognitive and emotional develop- ment; they also suggest that later inﬂuences, for example, during adolescence, may affect trajectories of development in these domains. www.annualreviews.org •Life Course Research on Adult Disease 19Annu. Rev. Public Health 2013.34:7-28. Downloaded from www.annualreviews.org by 144.82.38.29 on 09/25/14. For personal use only. PU34CH02-Power ARI 12 February 2013 19:27 This latter point is relevant to the argument that adolescence may provide a second sensitive developmental period when brains mature rapidly and new behaviors and capacities evolve (107). Lifelong enhancement and preservation of mental capacities are recognized as vital to meet future challenges for aging societies (25); hence, clariﬁcation of life course relationships and comorbidity with physical health remain important research priorities for birth cohorts.

INSIGHTS AND CHALLENGES One major insight from birth cohorts is that for several adult chronic diseases and functional outcomes, such as obesity, CVD biomarkers, and respiratory, cognitive, and mental func- tion, adult factors alone provide only a partial explanation. This observation is supported by research showing that factors from early-life stages (e.g., SEP, adiposity, mental health) are associated with later health and disease, even after controlling for adult factors. The tracking from earlier to later life of some functional and health indicators and health behaviors also suggests that inﬂuences on later outcomes are set in train earlier in life. Even when proximal factors in adulthood are paramount, the life course relationships leading to adulthood may, in some instances, be highly relevant. Thus, from a public health perspective, it matters for risk of type 2 diabetes that an adult has been obese for long periods of his or her lifetime (86) or, for lipid proﬁle, that the obese adult was lighter, relatively, at earlier life stages (74). Likewise, from the standpoint of several health and disease markers, including physical capability at older ages, the duration of disad- vantaged SEP throughout life is likely to matter for outcome. A focus solely on adulthood may not only provide incomplete or partial explana- tions for later health and disease, but in some instances lead to inﬂated estimates for adult factors. For example, adult psychosocial work stress and night working have been linked to adverse CVD risk factors, but associations di- minish after taking account of factors occurring before adult working life commences (becauseof the different backgrounds of individuals entering certain types of work) (105). Similarly, associations documented for adult cortisol and memory in later life attenuate when allowance is made for cognitive ability in childhood (most likely because of associations between cortisol and cognition earlier in life) (26). Such ﬁndings do not negate the role of adult life factors, which may compound inﬂuences from earlier in life, but they draw attention to the need to understand the impact of childhood and the intervening years to adult outcome.

Despite the rich data on development, function, health, and disease, few if any birth cohorts have the repeated measures needed to characterize life course trajectories of health/functional gain, peak, and decline, and the inﬂuences that drive them (Figures 1and 2). They can, however, be combined to show how aspects of health and function change over the life course. Findings from birth cohorts provide important clues, for example, on the full lifetime impact of SEP. At early stages of life, associations for SEP with several develop- mental, functional, and health indicators show delayed or poor development (e.g., for height as well as for emotional and cognitive ability), suggesting a failure to reach optimum or peak functioning among less socioeconomically advantaged groups even before advancing age- related declines occur. (The pattern is more commonly the reverse in terms of reproductive and many social careers, such as earlier school leaving, parenthood, and labor-market entry, for which less advantaged groups typically have accelerated trajectories compared with more advantaged groups.) Birth cohort study ﬁndings suggest that later in life there is earlier deterioration of physical capacity, faster health decline, and premature mortality among those from less advantaged SEP. Taken together, ﬁndings for different life stages of birth cohorts underscore the shorter healthy lifetimes of less advantaged SEP groups; contributions to this shortening healthy life appear to emanate from both earlier and later in life.

Notwithstanding challenges for birth cohort studies mentioned below, the wealth 20 Power ·Kuh ·MortonAnnu. Rev. Public Health 2013.34:7-28. Downloaded from www.annualreviews.org by 144.82.38.29 on 09/25/14. For personal use only. PU34CH02-Power ARI 12 February 2013 19:27 of information available from large groups of children and families repeatedly from birth and over time notionally provides relevant evidence about health and function across the life course with utility for the development of policy to improve population health and well-being.

Some changes to policy have been attributed to ﬁndings from birth cohorts, for example, in the New Zealand policy context relating to safety standards for children (e.g., compulsory wearing of cycle helmets) (98); in the United Kingdom, reassurance of supine sleeping position in babies was provided to support the Back to Sleep campaign (7), and evidence on the inﬂuence of socioeconomic position on life chances has been provided from several studies.

However, many challenges impede the attribu- tion of policy changes directly to ﬁndings from birth cohort studies. In the United Kingdom, the body of evidence in the developmental origins ﬁeld, often utilizing birth cohorts to demonstrate links between early-life and later outcomes, has not been directly credited with explicit policy development; however, it has been acknowledged as leading to an emphasis, in policy rhetoric at least, to the importance of early prevention strategies and has joined up intersectoral policy solutions to complex developmental issues (60). The ability of birth cohort studies to inform public health policy by providing evidence about the determinants of trajectories that lead to health and disease for a group of individuals as well as the ability to examine these trajectories in the context of the wider social and environmental conditions in which a particular population exists have perhaps been somewhat underutilized or underreported (113), although life course models often make this dimension explicit.

Among future challenges for birth cohort studies are the conceptual and analytic methods for handling the complexities of life course information together with neighborhood and macrolevel data. This general challenge embraces several related issues, including ade- quacy and historic relevance of measures, disen- tangling the direction of associations, causality, and related methodological approaches. BIOLOGICAL EMBEDDING Biological embedding, or embodiment, refers to the processes through which extrinsic factors experienced at different life stages “get under the skin,” i.e., alter the body’s biological functions or structures. These processes may involve development associated with critical periods, habituation, learning, damage, or repair.

Critical- or sensitive-period effects or biological embedding (see sidebar, Biological Embedding) will be difﬁcult to establish ﬁrmly without innovative biomarker or functional indicators to assess biological alterations. Ideally, in- dicator measures need to be appropriate for different ages to facilitate comparison across life stages and so that understand- ing can be advanced of individual health trajectories of gain and decline. Indicator measures for older birth cohorts that span childhood to adulthood include body size (birth weight, height, and weight) and cognitive, emotional, and sensory function; additional markers will be useful for younger cohorts. Likewise, accurate measurement of exposures and their timing is a challenge to existing and future studies, for example, to discrim- inate between inﬂuences on health gain and decline. A particular requirement, given the long-term design of birth cohorts, is that the meaning and social context of exposures are transparent many years after they are recorded. For example, infant feeding method may be recorded accurately as duration of breast- feeding, and yet decades later changes to infant formulas may be overlooked; the past meanings or associated motivations of maternal smoking in pregnancy or being born into and brought up in a large family may also differ from today.

The historic conditions of childhood that inevitably envelop long-term studies with follow-up from early life need to be understood and can affect interpretation www.annualreviews.org •Life Course Research on Adult Disease 21Annu. Rev. Public Health 2013.34:7-28. Downloaded from www.annualreviews.org by 144.82.38.29 on 09/25/14. For personal use only. PU34CH02-Power ARI 12 February 2013 19:27 but do not necessarily negate study ﬁndings or relevance to today. An agenda for life course research, to which birth cohort studies can contribute, includes addressing what the mechanisms are by which early-life events have long- term effects, and whether pathways can be altered or reversed; what the postnatal windows of plasticity are; the extent of intergenerational transmission of disease risk; the importance of developmental processes in generating the burden of disease in different populations; and what approaches are possible to intervene in individuals and in populations during different stages of the life course (27).

Particularly important from a policy perspective is the identiﬁcation of whether pathways from adverse early-life exposures to unfavorable outcomes can be altered and the life stages at which in- terventions might be most effective. Re- search on birth cohorts could contribute relevant knowledge, and, particularly with exploration of multiple cohorts born in different times or places, opportunities to examine issues of sensitivity to context are available. Methodological approaches to address questions about life course relationships are important to consider, given that exposure measures often correlate over time (13). Yet, despite rich data available for birth cohort studies, their observational designs raise the difﬁculty of inferring causality. Novel methodological approaches have been devised to shed light on causality, notably by using instrumental variables. Genetic instrumental variables, in particular, are now widely used to discriminate between causal and noncausal associ- ations (95), and their use is likely to extend with further genetic discoveries.

Not all exposures can be investigated using genetic instrumental variables, and further methodological development is needed to address causality. Meanwhile, birth cohorts can add to the totality ofthe evidence base, given that they are often better placed to take account of confounding than are other observational studies. In the context of research on healthy aging, investigators have argued that ﬁndings from longitudinal studies can add value by identifying problems to be addressed, can justify the need for a clinical trial, can suggest effect sizes on which to base the size of a trial and information to be collected, can indicate whether a risk factor affects some people more than others, and can provide a set of intermediate outcomes that predict onset of outcomes such as disability (31). Thus, an agenda on life course inﬂuences on aging is already emerging, which, given demographic trends in high-income countries, will be of enormous relevance to policy in forthcoming years. CONCLUDING REMARKS Findings from the maturing birth cohorts have fostered a life course approach to adult health, function, and disease, and in some instances have suggested the processes involved. Ad- vances in knowledge on unraveling life course relationships are not restricted to birth cohorts; i.e., other studies have contributed, although birth cohorts uniquely contain information from around the time of birth, have a broad range of data, and often follow up over several ages through childhood into adult life. Because birth cohort studies are observational, they provide information on associations rather than establish causality; hence, ﬁndings need to be considered together with evidence based on differing study designs. The growing number of cohorts born in different places and at differ- ent times provides increasing opportunities to establish the conditions under which life course relationships vary, providing further insights for public health. The birth cohorts suggest that broader socioeconomic conditions have a long-term impact across generations and throughout life. Researchers need to undertake a more complete exploration of the extent to 22 Power ·Kuh ·MortonAnnu. Rev. Public Health 2013.34:7-28. Downloaded from www.annualreviews.org by 144.82.38.29 on 09/25/14. For personal use only. PU34CH02-Power ARI 12 February 2013 19:27 which such risks to health acquired at earlier- life stages can be ameliorated by later protective factors. Further possibilities exist to test the extent to which adult health, function, and disease are affected by factors acting at different life stages, as well as the alternative processes through which life course relationships arise.In particular, the roles of developmental tra- jectories for life chances have been explored to some extent but will be important to consider for health outcomes later in life as cohorts age. New cohorts will address gaps in knowl- edge and assess whether relationships seen for previous generations apply to children of today.

DISCLOSURE STATEMENT The authors are not aware of any afﬁliations, memberships, funding, or ﬁnancial holdings that might be perceived as affecting the objectivity of this review.

ACKNOWLEDGMENTS The Great Ormond Street Hospital (GOSH)/University College London (UCL) Institute of Child Health was supported in part by the Department of Health’s National Institute for Health Research (NIHR) Biomedical Research Center. The Center for Pediatric Epidemiology and Biostatistics was supported in part by the MRC in its capacity as the MRC Center of Epidemiology for Child Health. DK is supported by the UK Medical Research Council. The Centre for Longitudinal Research—He Ara ki Mua was supported in part by the National Research Centre for Growth and Development (NRCGD) and by the University of Auckland and Auckland UniServices Limited. Growing Up in New Zealand is additionally supported by funding from multiple government agencies, led by the Ministry of Social Development.

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Obes.34:624–32 31. Guralnik M, Kritchevsky SB. 2010. Translating research to promote healthy aging: the complementary role of longitudinal studies and clinical trials.J. Am. Geriatr. Soc.58(Suppl. 2):S337–42 32. Hancox RJ, Milne BJ, Poulton R. 2004. Association between child and adolescent television viewing and adult health: a longitudinal birth cohort study.Lancet364:257–62 33. Hardy R, Kuh D. 2005. Social and environmental conditions across the life course and age at menopause in a British birth cohort study.Br.J.Obstet.Gynaecol.112:346–54 24 Power ·Kuh ·MortonAnnu. Rev. Public Health 2013.34:7-28. Downloaded from www.annualreviews.org by 144.82.38.29 on 09/25/14. For personal use only. PU34CH02-Power ARI 12 February 2013 19:27 34. Hardy R, Wadsworth M, Kuh D. 2000. The inﬂuence of childhood weight and socioeconomic status on change in adult body mass index in a British national birth cohort.Int. J. Obes.24:725–34 35. Hardy R, Wadsworth ME, Langenberg C, Kuh D. 2004. Birthweight, childhood growth, and blood pressure at 43 years in a British birth cohort.Int. J. Epidemiol.33:121–29 36. Henderson M, Hotopf M, Leon DA. 2009. Childhood temperament and long-term sickness absence in adult life.Br. J. Psychiatry194:220–23 37. Henderson M, Richards M, Stansfeld S, Hotopf M. 2012. The association between childhood cognitive ability and adult long-term sickness absence in three British birth cohorts: a cohort study.BMJ Open 2:e000777 38. Hertzman C, Power C, Matthews S, Manor O. 2001. Using an interactive framework of society and lifecourse to explain self-rated health in early adulthood.Soc. Sci. Med.53:1575–85 39. Horta BL, Barros FC, Victora CG, Cole TJ. 2003. Early and late growth and blood pressure in adoles- cence.J. Epidemiol. Commun. Health57:226–30 40. Horta BL, Gigante DP, Osmond C, Barros FC, Victora CG. 2009. Intergenerational effect of weight gain in childhood on offspring birthweight.Int. J. Epidemiol.38:724–32 41. Howe LD, Tilling K, Galobardes B, Smith GD, Gunnell D, Lawlor DA. 2012. Socioeconomic differences in childhood growth trajectories: At what age do height inequalities emerge?J. Epidemiol. Community Health66:143–48 42. Howe LD, Tilling K, Galobardes B, Smith GD, Ness AR, Lawlor DA. 2011. Socioeconomic disparities in trajectories of adiposity across childhood.Int. J. Pediatr. Obes.6:e144–53 43. Huxley RR, Shiell AW, Law CM. 2000. The role of size at birth and postnatal catch-up growth in determining systolic blood pressure: a systematic review of the literature.J. Hypertens.18:815–31 44. Inskip HM, Godfrey KM, Robinson SM, Law CM, Barker DPJ, Cooper C, SWS Study Group. 2006.

Cohort Proﬁle: the Southampton Women’s Survey.Int. J. Epidemiol.35:42–48 45. J ¨ arvelin MR, Sovio U, King V, Lauren L, Xu B, et al. 2004. Early life factors and blood pressure at age 31 years in the 1966 Northern Finland Birth Cohort.Hypertension44:838–46 46. Jefferis BJ, Power C, Hertzman C. 2002. Birth weight, childhood socioeconomic environment, and cognitive development in the 1958 British birth cohort study.BMJ325:305 47. Jokela M, Ferrie J, Kivim ¨ aki M. 2009. Childhood problem behaviors and death by midlife: the British National Child Development Study.J. Am. Acad. Child Adolesc. Psychiatry48:19–24 48. Jokela M, Power C, Kivim ¨ aki M. 2009. Childhood problem behaviors and injury risk over the life course.

J. Child Psychol. Psychiatry50:1541–49 49. Kaakinen M, L ¨ a ¨ ar ¨ a E, Pouta A, Hartikainen AL, Laitinen J, et al. 2010. Life-course analysis of a fat mass and obesity-associated (FTO) gene variant and body mass index in the Northern Finland Birth Cohort 1966 using structural equation modeling.Am. J. Epidemiol.172:653–65 50. Keating DP, Hertzman C, eds. 1999.Developmental Health and the Wealth of Nations: Social, Biological and Educational Dynamics.New York: Guilford 51. Kirkwood TB, Austad SN. 2000. Why do we age?Nature408:233–38 52. Kuh D, Ben-Shlomo Y, Lynch J, Hallqvist J, Power C. 2003. Life course epidemiology.J. Epidemiol.

Community Health57:778–83 53. Kuh D, Cooper R, Hardy R, Guralnik J, Richards M, Musculoskelet. Study Team. 2009. Lifetime cognitive performance is associated with midlife physical performance in a prospective national birth cohort study.Psychosom. Med.71:38–48 54. Kuh D, Pierce M, Adams J, Deanﬁeld J, Ekelund U, et al. 2011. Cohort proﬁle: updating the cohort proﬁle for the MRC National Survey of Health and Development: a new clinic-based data collection for ageing research.Int. J. Epidemiol.40:e1–9 55. Kuh D, Shah I, Richards M, Mishra G, Wadsworth M, Hardy R. 2009. Do childhood cognitive ability or smoking behaviour explain the inﬂuence of lifetime socio-economic conditions on premature adult mortality in a British post war birth cohort?Soc. Sci. Med.68:1565–73 56. Kumari M, Tabassum F, Clark C, Strachan D, Stansfeld S, Power C. 2008. Social differences in Insulin- like Growth Factor-1: ﬁndings from a British birth cohort.Ann. Epidemiol.18:664–70 www.annualreviews.org •Life Course Research on Adult Disease 25Annu. Rev. Public Health 2013.34:7-28. Downloaded from www.annualreviews.org by 144.82.38.29 on 09/25/14. For personal use only. PU34CH02-Power ARI 12 February 2013 19:27 57. Laitinen J, J ¨ a ¨ askel ¨ ainen A, Hartikainen AL, Sovio U, V ¨ a ¨ arasm ¨ aki M, et al. 2012. Maternal weight gain during the ﬁrst half of pregnancy and offspring obesity at 16 years: a prospective cohort study.BJOG 119:716–23 58. Laitinen J, Power C, Jarvelin MR. 2001. Family social class, maternal body mass index, childhood body mass index, and age at menarche as predictors of adult obesity.Am. J. Clin. Nutr.74:287–94 59. Langenberg C, Kuh D, Hardy R, Brunner E, Wadsworth MEJ. 2006. Social circumstances and educa- tion? Life course origins of social inequalities in metabolic risk in a prospective national cohort.Am. J.

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10:595–99 61. Lawlor DA, Batty GD, Morton SM, Deary IJ, Macintyre S, et al. 2005. Early life predictors of childhood intelligence: evidence from the Aberdeen Children of the 1950s Study.J. Epidemiol. Community Health 59:656–63 62. Leon DA, Lawlor DA, Clark H, Macintyre S. 2006. Cohort proﬁle: the Aberdeen Children of the 1950s study.Int. J. Epidemiol.35:549–52 63. Li L, Hardy R, Kuh D, Lo Conte R, Power C. 2008. Child-to-adult BMI and height trajectories: a comparison of two British birth cohorts.Am. J. Epidemiol.168:1008–15 64. Li L, Law C, Power C. 2007. Body mass index throughout the life-course and blood pressure in mid-adult life: a birth cohort study.J. Hypertens.25:1215–23 65. Mensah FK, Hobcraft J. 2008. Childhood deprivation, health and development: associations with adult health in the 1958 and 1970 British prospective birth cohort studies.J. Epidemiol. Community Health 62:599–606 66. Mishra G, Hardy R, Kuh D. 2007. Are the effects of risk factors for timing of menopause modiﬁed by age? Results from a British birth cohort study.Menopause14:717–24 67. Morton SMB. 2004. The child is the mother of the woman: intergenerational associations in maternal anthropometry.Int. J. Epidemiol.33:1249–51 68. Morton SMB. 2006. Maternal nutrition and fetal growth and development. InDevelopmental Origins of Health and Disease, ed. P Gluckman, M Hanson, pp. 98–129. Cambridge, UK: Cambridge Univ. Press 69. Morton SMB, Atatoa Carr PE, Grant CC, Robinson EM, Bandara DK, et al. 2012. Cohort pro- ﬁle: growing up in New Zealand.Int. J. Epidemiol.doi: 10.1093/ije/dyr206.http://ije.oxfordjournals.

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Am. J. Epidemiol.167:1332–41 104. Thomas C, Hypp ¨ onen E, Power C. 2007. Prenatal exposures and glucose metabolism in adulthood: Are effects mediated through birthweight and adiposity?Diabetes Care30:918–24 www.annualreviews.org •Life Course Research on Adult Disease 27Annu. Rev. Public Health 2013.34:7-28. Downloaded from www.annualreviews.org by 144.82.38.29 on 09/25/14. For personal use only. PU34CH02-Power ARI 12 February 2013 19:27 105. Thomas C, Power C. 2010. Do early life exposures explain associations in mid-adulthood between workplace factors and risk factors for cardiovascular disease?Int. J. Epidemiol.39:812–24 106. Viner RM, Cole TJ. 2006. Who changes body mass between adolescence and adulthood? Factors pre- dicting change in BMI between 16 year and 30 years in the 1970 British Birth Cohort.Int. J. Obes.

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failure to support the fetal origins hypothesis.Am. J. Epidemiol.155:849–52 113. Wise PH. 2009. Confronting social disparities in child health: a critical appraisal of life-course science and research.Pediatrics124:S203–11 28 Power ·Kuh ·MortonAnnu. Rev. Public Health 2013.34:7-28. Downloaded from www.annualreviews.org by 144.82.38.29 on 09/25/14. For personal use only. PU34-FrontMatter ARI 19 February 2013 13:11 Annual Review of Public Health Volume 34, 2013 Contents Symposium: Developmental Origins of Adult Disease Commentary on the Symposium: Biological Embedding, Life Course Development, and the Emergence of a New Science Clyde Hertzman 1 From Developmental Origins of Adult Disease to Life Course Research on Adult Disease and Aging: Insights from Birth Cohort Studies Chris Power, Diana Kuh, and Susan Morton 7 Routine Versus Catastrophic Inﬂuences on the Developing Child Candice L. Odgers and Sara R. Jaffee 29 Intergenerational Health Responses to Adverse and Enriched Environments Lars Olov Bygren 49 Epidemiology and Biostatistics Commentary on the Symposium: Biological Embedding, Life Course Development, and the Emergence of a New Science Clyde Hertzman 1 From Developmental Origins of Adult Disease to Life Course Research on Adult Disease and Aging: Insights from Birth Cohort Studies Chris Power, Diana Kuh, and Susan Morton 7 Causal Inference in Public Health Thomas A. Glass, Steven N. Goodman, Miguel A. Hern ´ an, and Jonathan M. Samet 61 Current Evidence on Healthy Eating Walter C. Willett and Meir J. Stampfer 77 Current Perspective on the Global and United States Cancer Burden Attributable to Lifestyle and Environmental Risk Factors David Schottenfeld, Jennifer L. Beebe-Dimmer, Patricia A. Bufﬂer, and Gilbert S. Omenn 97 viiiAnnu. Rev. Public Health 2013.34:7-28. Downloaded from www.annualreviews.org by 144.82.38.29 on 09/25/14. For personal use only. PU34-FrontMatter ARI 19 February 2013 13:11 The Epidemiology of Depression Across Cultures Ronald C. Kessler and Evelyn J. Bromet 119 Routine Versus Catastrophic Inﬂuences on the Developing Child Candice L. Odgers and Sara R. Jaffee 29 Intergenerational Health Responses to Adverse and Enriched Environments Lars Olov Bygren 49 Environmental and Occupational Health Intergenerational Health Responses to Adverse and Enriched Environments Lars Olov Bygren 49 Causal Inference Considerations for Endocrine Disruptor Research in Children’s Health Stephanie M. Engel and Mary S. Wolff 139 Energy and Human Health Kirk R. Smith, Howard Frumkin, Kalpana Balakrishnan, Colin D. Butler, Zo¨ e A. Chafe, Ian Fairlie, Patrick Kinney, Tord Kjellstrom, Denise L. Mauzerall, Thomas E. McKone, Anthony J. McMichael, and Mycle Schneider 159 Links Among Human Health, Animal Health, and Ecosystem Health Peter Rabinowitz and Lisa Conti 189 The Worldwide Pandemic of Asbestos-Related Diseases Leslie Stayner, Laura S. Welch, and Richard Lemen 205 Transportation and Public Health Todd Litman 217 Public Health Practice Implementation Science and Its Application to Population Health Rebecca Lobb and Graham A. Colditz 235 Promoting Healthy Outcomes Among Youth with Multiple Risks:

Innovative Approaches Mark T. Greenberg and Melissa A. Lippold 253 Prospects for Tuberculosis Elimination Christopher Dye, Philippe Glaziou, Katherine Floyd, and Mario Raviglione 271 Rediscovering the Core of Public Health Steven M. Teutsch and Jonathan E. Fielding 287 Contents ixAnnu. Rev. Public Health 2013.34:7-28. Downloaded from www.annualreviews.org by 144.82.38.29 on 09/25/14. For personal use only. PU34-FrontMatter ARI 19 February 2013 13:11 Social Environment and Behavior Routine Versus Catastrophic Inﬂuences on the Developing Child Candice L. Odgers and Sara R. Jaffee 29 HIV Prevention Among Women in Low- and Middle-Income Countries: Intervening Upon Contexts of Heightened HIV Risk Steffanie A. Strathdee, Wendee M. Wechsberg, Deanna L. Kerrigan, and Thomas L. Patterson 301 Scaling Up Chronic Disease Prevention Interventions in Lower- and Middle-Income Countries Thomas A. Gaziano and Neha Pagidipati 317 Stress and Cardiovascular Disease: An Update on Current Knowledge Andrew Steptoe and Mika Kivim ¨ aki 337 The Impact of Labor Policies on the Health of Young Children in the Context of Economic Globalization Jody Heymann, Alison Earle, and Kristen McNeill 355 Commentary on the Symposium: Biological Embedding, Life Course Development, and the Emergence of a New Science Clyde Hertzman 1 From Developmental Origins of Adult Disease to Life Course Research on Adult Disease and Aging: Insights from Birth Cohort Studies Chris Power, Diana Kuh, and Susan Morton 7 Intergenerational Health Responses to Adverse and Enriched Environments Lars Olov Bygren 49 Promoting Healthy Outcomes Among Youth with Multiple Risks:

Innovative Approaches Mark T. Greenberg and Melissa A. Lippold 253 The Behavioral Economics of Health and Health Care Thomas Rice 431 Health Services Reducing Hospital Errors: Interventions that Build Safety Culture Sara J. Singer and Timothy J. Vogus 373 Searching for a Balance of Responsibilities: OECD Countries’ Changing Elderly Assistance Policies Katherine Swartz 397 xContentsAnnu. Rev. Public Health 2013.34:7-28. Downloaded from www.annualreviews.org by 144.82.38.29 on 09/25/14. For personal use only. PU34-FrontMatter ARI 19 February 2013 13:11 Strategies and Resources to Address Colorectal Cancer Screening Rates and Disparities in the United States and Globally Michael B. Potter 413 The Behavioral Economics of Health and Health Care Thomas Rice 431 Scaling Up Chronic Disease Prevention Interventions in Lower- and Middle-Income Countries Thomas A. Gaziano and Neha Pagidipati 317 Indexes Cumulative Index of Contributing Authors, Volumes 25–34 449 Cumulative Index of Article Titles, Volumes 25–34 454 Errata An online log of corrections to Annual Review of Public Health articles may be found at http://publhealth.annualreviews.org/ Contents xiAnnu. Rev. Public Health 2013.34:7-28. Downloaded from www.annualreviews.org by 144.82.38.29 on 09/25/14. For personal use only. Annu A l Reviews it’s about time. Your time. it’s time well spent. Annu Al Reviews | Connect with Our experts Tel: 800.523.8635 (us/can ) | Tel: 650.493.4400 | Fax: 650.424.0910 | Email: [email protected] New From Annual Reviews:

Annual Review of Statistics and Its Application Volume 1 • Online January 2014 • http://statistics.annualreviews.org Editor: Stephen E. Fienber g, Carnegie Mellon University Associate Editors: Nancy Reid, University of Toronto Stephen M. Stigler, University of Chicago The Annual Review of Statistics and Its Application aims to inform statisticians and quantitative methodologists, as well as all scientists and users of statistics about major methodologica\ l advances and the computational tools that allow for their implementation. It will include developments in the field of statistics, including theoretical statistical underpinnings of new methodology, as well as developments in specific application domains such as biostatistics and bioinformatics, economics, machine learning, psychology, sociology, and aspects of the physical sciences.

Complimentary online access to the first volume will be available until January 2015.

table of contents : • What Is Statistics? Stephen E. Fienberg • A Systematic Statistical Approach to Evaluating Evidence from Observational Studies, David Madigan, Paul E. Stang, Jesse A. Berlin, Martijn Schuemie, J. Marc Overhage, Marc A. Suchard, Bill Dumouchel, Abraham G. Hartzema, Patrick B. Ryan • The Role of Statistics in the Discovery of a Higgs Boson, David A. van Dyk • Brain Imaging Analysis, F. DuBois Bowman • Statistics and Climate, Peter Guttorp • Climate Simulators and Climate Projections, Jonathan Rougier, Michael Goldstein • Probabilistic Forecasting, Tilmann Gneiting, Matthias Katzfuss • Bayesian Computational Tools, Christian P. Robert • Bayesian Computation Via Markov Chain Monte Carlo, Radu V. Craiu, Jeffrey S. Rosenthal • Build, Compute, Critique, Repeat: Data Analysis with Latent Variable Models, David M. Blei • Structured Regularizers for High-Dimensional Problems:

Statistical and Computational Issues, Martin J. Wainwright • High-Dimensional Statistics with a View Toward Applications in Biology, Peter Bühlmann, Markus Kalisch, Lukas Meier • Next-Generation Statistical Genetics: Modeling, Penalization, and Optimization in High-Dimensional Data, Kenneth Lange, Jeanette C. Papp, Janet S. Sinsheimer, Eric M. Sobel • Breaking Bad: Two Decades of Life-Course Data Analysis in Criminology, Developmental Psychology, and Beyond, Elena A. Erosheva, Ross L. Matsueda, Donatello Telesca • Event History Analysis, Niels Keiding • Statistical Evaluation of Forensic DNA Profile Evidence, Christopher D. Steele, David J. Balding • Using League T able Rankings in Public Policy Formation:

Statistical Issues, Harvey Goldstein • Statistical Ecology, Ruth King • Estimating the Number of Species in Microbial Diversity Studies, John Bunge, Amy Willis, Fiona Walsh • Dynamic Treatment Regimes, Bibhas Chakraborty, Susan A. Murphy • Statistics and Related Topics in Single-Molecule Biophysics, Hong Qian, S.C. Kou • Statistics and Quantitative Risk Management for Banking and Insurance, Paul Embrechts, Marius Hofert Access this and all other Annual Reviews journals via your institution at www.annualreviews.org. Annu. Rev. Public Health 2013.34:7-28. Downloaded from www.annualreviews.org by 144.82.38.29 on 09/25/14. For personal use only.