[PDF] - Fertility History and Biomarkers using Prospective Data: Evidence PDF Document

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1 Fertility History and Biomarkers using Prospective Data: Evidence from the 1958 National Child Development Study Maria Sironi University College London, Department of Social Science, Email: m.sironi@ucl.ac.uk George B. Ploubidis University College London, Department of Social Science & Center for Longitudinal Studies Emily Grundy London School of Economics, Department of Social Policy, & ISER, University of Essex Abstract Previous research on possible later life health implications of fertility history has predominantly considered associations with mortality or self-reported indicators of health. Using the 1958 National Child Development Study, and in particular the biomedical survey conducted in 2002-2003, we study the relationship between fertility histories – considering number of children, age at first and age at ‘last’ birth – and biomarkers for cardiovascular disease and respiratory function among both men and women. Results show that there is a relationship between fertility histories and these objective indicators of health, and key associations are with age at first and at last birth, rather than with number of children. Specifically, there is an inverted J-shape relationship between age at first birth and biomarkers, with worse outcomes for very young ages (and for some of them also for `very

ld' ages). A very low age at last birth is associated with negative health outcomes, especially

among women.

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2 Introduction Parenthood frequently leads to major changes in activities, lifestyles and allocation of resources and for women pregnancy, parturition and lactation involve considerable physiological changes. It is therefore not surprising that a growing literature indicates linkages between fertility history and later life health and mortality. In general, previous studies indicate a J shaped association between overall parity and mortality with higher risks for childless and high parity parents compared to parents of two or three children (for reviews see Hurt, Ronsmans, and Thomas 2006; Zeng et al. 2016; Högnäs et al. 2017). Although many studies consider only women, those that include men tend to report similar, albeit less strong, associations suggesting underlying biosocial processes, as well as specific physiological effects which apply only to women. Timing of parenthood has been shown to be important with many studies indicating increased later life mortality and poorer health

utcomes among those entering parenthood at a young age, although with some contextual

variations (Grundy and Foverskov 2016). Mechanisms underlying these associations are hypothesized to include a range of partly offsetting factors (Grundy and Tomassini 2005). On the positive side, children provide an incentive to healthier behaviours and a source of social interaction and support during both childrearing and subsequent phases of life. Less positively, parenthood involves stresses, including for women the stress of pregnancy, parturition and lactation, and substantial economic costs. Cumulative effects of these stresses may outweigh salutogenic effects of parenthood especially for young parents - who may be less resilient to stress and have fewer social and economic resources (Falci, Mortimer, and Noel 2010) -, those with closely spaced births (Grundy and Kravdal 2010) and large family sizes (D’Elio et al. 1997). Moreover, early parenthood may lead to disruption of educational and career progression and increased risk of partnership breakdown, and so to socio-

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3 economic and social disadvantage (Grundy and Read 2015). Finally, although there is evidence that parenthood may be associated with less risky behaviors, some studies suggest a positive association with obesity. A further complicating factor is the need to account for selection to fertility pathways; early parenthood, for example, is associated with childhood disadvantage. The complexity of these associations means that our understanding of underlying processes and mechanisms is still limited. The aim of this paper is to contribute to our understanding of these by examining associations between aspects of fertility histories and biomarkers indicative of health status, which may mediate progression to later disability and mortality. On one hand, the positive effects of parenthood (such as greater social interaction and support, and social control of health-related behaviors) may imply, for example, less smoking among parents, and this may be associated with lower risks for cardiovascular disease and with better respiratory function. On the other hand, the negative effects of some patterns of childbearing and rearing (such as greater stress associated with early parenthood and high parity) may have negative implications for these indictors of health. Higher parity, for example, has been found to lead to higher risks of obesity, a well-established cardiovascular risk factor (Sowers 2003). One of the main problems when studying these associations is that these effects may be offsetting, and also there are major selection processes in place (in particular in relation to childhood health and early life socioeconomic conditions). Therefore, it is essential to use high quality prospective data which allows consistency across related outcomes to be investigated. In this paper, we use prospective data from the 1958 National Child Development Survey (NCDS), combining information from sweeps 0 (1958) to 7 (2004 – age 46), and from the biomedical survey collected in 2002 (age 44). We look at several aspects of

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4 parenthood trajectories, including parity and age at first and at last birth, and at multiple biomarkers for cardiovascular disease and respiratory function. Previous research The existing literature on the relationship between fertility and later-life health has focused partly on the association between the number of children and all cause or cause specific mortality risks (Dior et al. 2013; Doblhammer 2000; Grundy and Kravdal 2007, 2010; Grundy and Tomassini 2006; Hinkula et al. 2005; Hurt et al. 2006; Jaffe et al. 2009; Jaffe, Eisenbach, and Manor 2011; Tamakoshi et al. 2010), and has generally shown a J-shaped relationship between parity and mortality. Specifically, individuals that have two or three children show a lower mortality risk than those who are childless, have one child, or have four or five or more children. Many studies are restricted to women but results from those including men suggest that, although associations may be a bit weaker, the J-shaped relationship is still observed. The similarity of findings for men and women suggests biosocial pathways underlying associations between fertility and health (Grundy and Kravdal 2007). Some studies have investigated not only number of children, but also other fertility trajectory characteristics such as age at first and last birth and experience of multiple births

r short inter-birth intervals (Grundy and Kravdal 2014), or combined effects of fertility and

partnership histories (Kravdal et al. 2012). Many of these studies indicate that early parenthood is associated with a higher risk of later life mortality. Although this relationship is partially explained by socioeconomic background, health related factors and, in the US, ethnicity (Grundy 2009; Kravdal et al. 2012; Spence and Eberstein 2009), analyses controlling for these influences, including sibling comparison studies (Barclay et al. 2016), also find an adverse association between early parenthood and later mortality risks.

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5 Together with evidence on mortality, recent research has focused on associations between fertility and other health outcomes, both in midlife and at older ages. (Buber and Engelhardt 2008; Grundy and Holt 2000; Grundy and Tomassini 2005; Gunes 2016; Hank 2010; Hanson, Smith, and Zimmer 2015; Henretta 2007; O’ Flaherty et al. 2016; Pirkle et al. 2014; Read, Grundy, and Wolf 2011; Williams et al. 2011). Outcomes investigated include self-reported health, disability, presence of limiting long-term illness, and chronic diseases. Other studies have also examined psychological well-being and mental health (Grundy and Read 2015; Henretta et al. 2008; Spence 2008). Findings are similar to those reported for mortality, with early parenthood, childlessness, or high parity associated with poorer health

utcomes. Conversely a later age at first parenthood is associated with better health later in
life. As for mortality, the relationship between fertility and health is partially confounded or

mediated by life course socio-economic factors and partnership status with some evidence of contextual influences (Grundy and Foverskov 2016). Most studies so far have relied on self-reported measures of health. These measures have some limitations, for example they may be biased and influenced by health expectations, which are also correlated with socioeconomic status. Recently, objective and so more unbiased measures have become available to researchers, thanks to the collection of blood samples and interviews with nurses and doctors included in surveys. Few studies have looked specifically at the association between fertility and biomarkers. Hardy et al. (2007) employed the 1946 British birth cohort and investigated the association between number of children and coronary heart disease risk factors, using blood pressure, body mass index (BMI), waist to hip ratio (WHR), total cholesterol, high-density lipoprotein and low-density lipoprotein cholesterol and triglyceride levels, and glycated haemoglobin at age 53. They did not find any consistent relationship between parity and these biomarkers, and the

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6 associations were mostly explained by behavior and lifestyle. Grundy and Read (2015) looked at the link between retrospectively collected fertility histories and allostatic load (and long-term illness) among people born before 1952 in England. The measure of allostatic load was derived using 9 biomarkers: blood pressure, WHR, peak expiratory flow, HDL/total cholesterol ratio (mg/dL), triglycerides, glycosylated haemoglobin, fibrinogen, and C-reactive protein. They found that earlier ages at first birth were associated with worse allostatic load, but the relationship was mediated in part by wealth, physical activity and

smoking. Also, there was no association between childlessness and allostatic load.

The results of research focusing on biomarkers and objective measures of health, thus are not always consistent with the findings from studies using self-reported health

utcomes, generally with a stronger relationship between parity and health when using self-

reported measures compared to biomarkers. We build on this past research, and try to shed some light on the connection between fertility histories and several biomarkers representing cardiovascular disease risk factors and respiratory functions risk factors. Data and Methods Data Data used for the analysis are taken from several waves of the 1958 NCDS. The NCDS started in 1958 and follows the life of more than 17,000 infants born in England, Scotland and Wales in a single week in March 1958. The study has collected information on multiple domains, such as physical and educational development, economic circumstances, employment, family life, health behaviour, etc. Ten additional sweeps have been collected since 1958 when respondents were aged 7, 11, 16, 23, 33, 42, 46, 50, and 55. In 2002, when respondents were 44-45 years old, a biomedical survey was collected for more than 9,000

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respondents. This survey collected objective measures of health, blood samples were

collected from 88% of those examined, and 8018 blood samples were received from subjects who gave consent to extraction of DNA. In this work we make use of the biological markers

btained from this survey, and combine these data with information collected in other

sweeps to obtain information on fertility histories (from age 23, 33, 42, and 46) and background information on the respondents, i.e. on early life health and socioeconomic conditions, and cognitive abilities during childhood. A complete case sample including respondents who provided blood samples, and information on fertility histories, and all background variables, would comprise only 2,506 individuals (51% of them women). Given the large number of missing values, we perform multiple imputation that allows us to retain a sample of 15,252 respondents for the parity analysis (49% of which are women), and of 11,754 for age at first and ‘last’ birth (51.5% of which are women). Methods Measures of Health In order to study the relationship between fertility and health, we selected specific measures

f health that are related to the potential mechanisms mentioned in the introduction. In

particular, we selected measures that are related to accumulated stress and also to lifestyle factors (e.g. smoking and eating habits). Hence, the analysis focuses on two major health

utcomes: cardiovascular disease risk factors and respiratory function. The biomarkers used

to investigate cardio-metabolic risk are:

Fibrinogen: a marker of inflammation and cardiovascular disease (g/L);
C-reactive protein: an indicator of inflammation and cardiovascular disease (g/L);

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Glycated haemoglobin (HbA1c): index of glucose metabolism over the previous 30−90

days, correlated with the presence of diabetes mellitus;

Cholesterol Ratio: Total cholesterol/HDL ratio, index of risk for cardiovascular

disease;

High blood pressure: three measures of systolic and diastolic blood pressure were taken.

The mean of valid readings was used, and an individual was recorded as having high blood pressure if the average value was above 140/90;

Obesity: the body mass index was calculated using information on height and weight,

with obesity defined as a BMI greater than 30;

Waist to Hip Ratio (WHR): waist and hip circumferences were measured and the ratio
f waist over hip calculated.

To assess respiratory function we used forced expiratory volume (FEV1), with the highest measurement used as a valid one. FEV1 is a measure of how much air a person can exhale during forced breath during the first second. Fertility History To have a general picture of individuals’ fertility trajectories we consider not only the number of natural children (by age 44), but also age at first birth, and the age at ‘last’ birth (last birth before the biomedical survey, it is possible that some individuals in the sample had children after age 44). As the relationship between parity and health is non-linear, we used a categorical variable for number of children with 5 possible values: childless, 1 child, 2 children, 3 children, 4 or more children. Age at first birth was categorized into bands which are slightly different for men and women, based on results from previous literature and the distribution in our sample: less than 20, between 20 and 24, between 25 and 29, between 30 and 34, and

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9 more than 35 among women; less than 23, between 23 and 27, between 28 and 32, between 33 and 38, and more than 39 among men. Age at ‘last’ birth is also a categorical variable with five possible values (the same for men and women): less than 25, between 25 and 29, between 30 and 34, between 35 and 39, and greater than 40. Control Variables Health outcomes and fertility variables may be influenced by other confounders that need to be taken into account in the analysis of the association between health and fertility. Childhood health and socioeconomic conditions have been found to be associated both with adult health (Haas 2007, 2008; Nyström Peck 1994; Rahkonen, Lahelma, and Huuhka 1997) and with fertility trajectories, especially timing of birth (Geronimus and Korenman 1992; Penman-Aguilar et al. 2013). Using information collected at age 0, 7, 11, and 16 we can take into account early life health and socioeconomic background measures1. In particular, we consider social class at birth, based on father’s occupation: manual (‘skilled manual’, ‘partly skilled’, ‘unskilled’, ‘other/unknown’) vs. non-manual (i.e. ‘professional’, ‘managerial’, ‘skilled non-manual’), parental years of education (either mother’s or father’s – whichever was higher if both available), and if the respondents’ mother stayed in school after minimum leaving age. We also consider living conditions at age 11, and we look at financial hardship during the past year when the respondent was 11, if there was overcrowding in the household (more than 1.5 persons per room), and at their housing conditions (lacking access to a bathroom, and/or an indoor WC, and/or cooking facilities, and/or hot water, vs. having access to all). In order to take into account

1 Most of the early life conditions and childhood health were asked to the parents: social class at birth, parental

education, mother staying in school after minimum age, financial hardship, overcrowding, housing conditions, birth weight, mother smoking during pregnancy, if the child was out of school for more than a month, the number of times the child was hospitalized, enuresis problems, physical coordination problems, the Rutter Behaviour Scale, family difficulties and parental divorce, parental interest in child’s education. Two of the control variables were asked to the teachers: the BSAG score and the general ability score. Finally, all the other variables were asked to the main respondent.

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10 health conditions of the respondents during childhood we consider birth weight, if the mother was smoking during pregnancy, if they were out of school for a month or more due to health problems, the number of times they have were hospitalized before age 11, episodes of enuresis at age 7 and age 11, and poor physical coordination at age 11. The prospective nature of our data allows us to include confounders that are rarely found in other studies, such as mental health at age 7 and 11: we include the Bristol Social Adjustment Guide score completed by the teacher2 at age 11, the Rutter Behaviour Scales completed by the mother at age 7 and 113. We also consider possible family disruptions during childhood: we include a variable indicating family difficulties (i.e. divorce, separation, and desertion) at age 7, and one indicating parental divorce before age 11. In order to take into account possible risky health behaviours during adolescence, we consider whether the respondent was smoking at age 16. Moreover we include a measure of cognitive ability at age 11, i.e. a general ability test score4 consisting of 40 verbal and 40 non- verbal items (0-80), and parents interest in the respondent education at age 11 (interested vs. not interested). Finally, we include their level of education at age 23, the number of marriages and cohabitations by age 42 (none, one, or more than one), the number for months unemployed between January 1978 and December 2001, and age at the biomedical interview.

2 The Total 'Syndrome' scores are meant to give a quantitative assessment of the behaviour pattern in question.

The Syndrome totals are added together to give a score which indicates, fairly crudely, the total amount of behavioural deviance (or maladjustment?) as measured by the Guide. The behaviours taken into account are: Unforthcomingness, Withdrawal, Depression, Anxiety for acceptance by adults, Hostility towards adults, 'Writing off' of adults and adult standards, Anxiety for acceptance by children, Hostility towards children, Restlessness, 'Inconsequential' behaviour, Miscellaneous symptoms, Miscellaneous nervous symptoms.

3 We include the z-scores for psychological distress, built using an internalizing score from summing 5 items

(worried, solitary, miserable, fearful, and fussy) and an externalizing score from summing 9 items (destructive, fights, not liked, irritable, disobedient, lies, steals, bullies, and resentful/aggressive) (Winning et al. 2015).

4 Children were tested individually by teachers, who recorded the answers for the tests. For the verbal items,

children were presented with an example set of four words that were linked either logically, semantically, or

phonologically. For the non-verbal tasks, shapes or symbols were used. The children were then given another

set of three words or shapes or symbols with a blank. Participants were required to select the missing item from a list of five alternatives.

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11 Analytical Strategy We first produce some descriptive statistics that describe the sample and the variables used in the analysis. To maximize representativeness, the descriptive statistics are based on the specific sample available at the sweep in which the variable was collected (e.g. the sample available at the biomedical sweep for biomarkers), and for variables built using multiple sweeps we consider the sample for which the information is available by the age the biomedical sweep is conducted. Before running the multivariable analysis, we performed Multiple Imputation with chained equations with 50 imputed datasets using all variables in the substantive model as well as auxiliary variables (see Appendix) in the imputation process(Carpenter and Kenward 2012). Multiple imputation operates under the Missing at Random (MAR) assumption (Little and Rubin 2014), which in this case implies that our estimates are valid if missingness is due to variables included in the imputation phase. We then run multivariate linear or logistic (depending on the outcome) regressions for all our health measures and each of the fertility characteristics, controlling for all the variables mentioned above. Since childbearing has a potential different impact on health – due to physiological mechanisms and to who takes care of the child(ren) – on women compared to men, all the analysis are performed separately by gender. Finally, in order to detect potential confounding, and measurement error, and to make sure that the selection into fertility pathways is not biasing the results, we use the ‘negative controls’ technique designed to detect both suspected and unsuspected sources of spurious causal inference (Lipsitch, Tchetgen, and Cohen 2010). Negative controls test the ability of the controls that were included in the regressions to produce a null association between the exposure and the negative control outcome, taking into account that there is no

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12 plausible mechanism of action that links them other than confounding and/or measurement

error. As ‘negative controls’ we use three variables from the 2002 biomedical survey: hair color

(light brown and blond vs. dark brown and black), ear tested first (left vs. right), and arm blood taken from (left vs. right). The results are reported in the Appendix. Results Descriptive Statistics Table 1 reports information on health risk factors for cardiovascular diseases and respiratory

function. A normal range of fibrinogen (protein that helps coagulation) is between 2.0 and

4.0 g/L. The average in our sample is in the normal range, and slightly higher for women. Also the average value of the C-reactive protein (between 1.0 and 3.0 g/L) and of the glycated haemoglobin (below 6%) is in the normal range.

Table 1. Health Measures Men Women Mean

r %

SD N Mean

r %

SD N Cardio-metabolic Risk Factors Fibrinogen (g/L) 2.88 (0.58) 3,845 3.03 (0.65) 3,838 C-Reactive Protein (g/L) 1.97 (4.40) 3,856 2.38 (4.16) 3,836 Glycated Haemoglobin 5.32 (0.76) 3,976 5.19 (0.63) 3,947 Total Cholesterol 6.07 (1.14) 3,927 5.70 (1.00) 3,897 HDL Cholesterol 1.43 (0.34) 3,914 1.69 (0.41) 3,894 LDL Cholesterol 3.57 (0.93) 3,571 3.29 (0.87) 3,820 Cholesterol Ratio (Total/HDL) 4.42 (1.19) 3,914 3.54 (1.00) 3,893 % with High Blood Pressure 16.0 4,608 5.58 4,622 BMI 27.8 (4.27) 4,585 26.9 (5.53) 4,625 % Obese 25.3 4,585 23.5 4,625 Waist to Hip Ratio 0.93 (0.06) 4,629 0.81 (0.06) 4,670 % Obese using WHR 34.0 4,629 25.1 4,670 Respiratory Functions Forced Expiratory Volume (lt.) 3.73 0.83 4,522 2.75 0.60 4,568 N in Biomedical Survey 4,665 4,712

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13 Men on average had a cholesterol ratio higher than a normal value (~3.5), and 16% had high blood pressure compared with only 5.6% of women. Between 23% and 25% of the sample was obese according to their BMI, but when we consider the WHR 25% of women and 34%

f men are obese.

Table 2 describes the fertility histories available for men and women at the time of the biomedical sweep. 80.6% of men and 85.2% of women had had a child by age 44. On average they had 1.73 and 1.90 children, respectively, with 36.4% of men and 40.8% of women having 2 children. Around 3% had had twins among both men and women.

Table 2. Fertility Measures Men Women Mean or % SD Mean or % SD % Ever had a child - Age Biom. 80.6 85.2 Number of Children - Age Biom. 1.73 (1.24) 1.90 (1.22) 19.7 14.9 1 20.6 18.2 2 36.4 40.8 3 16.3 18.1 4+ 7.0 8.0 % with Twins - Age Biom. 2.48 2.75 Age at First Birth (Mean) 27.9 (5.70) 25.4 (5.49) Age at First Birth - Distribution (%) <23(M) / <20(W) 22.4 16.8 23-27 (M) / 20-24 (W) 31.4 35.6 28-32 (M) / 25-29 (W) 26.5 27.3 33-38 (M) / 30-34 (W) 15.6 14.1 39+ (M) / 35+ (W) 4.1 6.1 Age at Last Birth - Avg. 32.1 (5.34) 30.1 (5.32) Age at Last Birth - Distribution (%) <25 9.2 17.7 25-29 27.2 32.4 30-34 33.7 30.4 35-39 21.3 15.8 40+ 8.7 3.8 N with info on fertility history at Biomed. Sweep 6,048 (Age 1st: 4,854; Age last: 3,608) 6,356 (Age 1st: 5,407; Age last: 4,253)

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14 The age at first birth was 2.5 years higher for men (27.9) than for women (25.4), and one third of the male sample had the first child between age 23 and age 27, while one third of the female sample had the first child between 20 and 24. Age at ‘last’ birth was 2 years higher for men (32.1 for men and 20.1 for women), and 33.7% of men and 30.4% of women had their ‘last’ child when they were between 30 and 34 years old. Finally, Table 3 shows the background and early life characteristics of the sample. 68% of the sample came from a social class of skilled manual works or lower social class, and the average number of years of education for parents is 11. 11% of study members’ parents reported financial hardship when the study child was aged 11 years, and 12% reported overcrowding in the household. Almost 11% of the sample lacked one or more of these facilities in their accommodation: a bathroom, indoor WC, cooking facilities and hot

water. Between 4 and 5% reported family difficulties at age 7, and parental divorce by age 11.

The average birth weight of the respondents is in the normal range, 119 ounces (3.4kg) for boys and 114 ounces (3.2kg) for girls. One third of respondents’ mothers had smoked during pregnancy and 5% of both men and women has been out of school for a month or more – before age 11 – because of health problems. The BSGA average score is 9.9 for boys and 7 for girls. We standardized the Rutter Scale scores since it is a latent variable. 37.5% of boys and 34% of girls were smoking (1 or more cigarettes per week) at age 16. The general ability test average scores are 41.8 for boys and 44.1 for girls. Between 76% and 78% of parents were interested in their child’s education when the child was 11 years old. 19% of respondents reported a high level of education at age 23, i.e. a university degree (or equivalent) or more.

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Table 3. Control Variables Men Women Mean

r %

SD N N in the Sweep Mean

r %

SD N N in the Sweep Early Life Socioeconomic Background Social Class at Birth - % Manual 68.2 7,227 9,004 68.1 6,887 8,411 % in Financial Hardship - Age 11 11.1 6,857 7,887 11.6 6,506 7,450 % Overcrowding - Age 11 12.0 7,065 7,887 12.1 6,727 7,450 Housing: % NO Access to 1+ (Bathroom; Indoor WC; Cooking Facilities; Hot water) - Age 11 10.82 6,989 7,887 10.9 6,640 7,450 % with Family Difficulties - Age 7 4.46 7,152 7,917 4.03 6,794 7,508 % with Divorced Parents - Age 11 4.53 7,886 7,887 4.66 7,450 7,450 % Mother in School after Minimum Age - Age 0 24.8 8,970 9,004 25.1 8,383 8,411 Parents' Years of Education - Age 16 11.3 (1.81) 5,901 7,547 11.4 (1.84) 5,623 7,107 % Parents interested in R Education - Age 11 76.1 7,206 7,887 77.9 6,830 7,450 Early Life Health Birth Weight (ounces) 119.0 (22.0) 8,959 9,004 114.1 (21.1) 8,382 8,411 % Mother smoking when pregnant 33.2 9,004 9,004 33.2 8,411 8,411 % Out of school for 1+ months - Age 11 4.88 7,803 7,887 5.51 7,370 7,450 # times hospitalized - Age 11 0.74 (0.98) 7,089 7,887 0.57 (0.85) 6,736 7,450 % with Enuresis - Age 7 14.0 7,467 7,917 11.6 7,074 7,508 % with Enuresis - Age 11 7.37 7,069 7,887 4.73 6,723 7,450 Physical Coordination Problems - Age 11 17.2 7,028 7,887 13.5 6,624 7,450 BSGA Tot 'Syndrome' Score - Age 11 9.88 (9.67) 7,273 7,887 7.03 (7.95) 6,883 7,450 Rutter Scale - Age 7

0.01

(1.00) 7,506 7,917 0.01 (1.00) 7,102 7,508 Rutter Scale - Age 11 0.06 (1.01) 7,078 7,887

0.07

(0.98) 6,727 7,450 % Smoking - Age 16 37.5 6,114 7,547 34.0 5,855 7,107 Cognitive Ability General ability test score - Age 11 41.8 (16.3) 7,253 7,887 44.1 (15.9) 6,878 7,450 Education Level - Age 23 % Low 25.5 5,205 6,267 29.8 5,457 6,270 % Medium 56.0 50.7 % High 18.5 19.5 Sociodemographic Characteristics Age at Interview - Biomed Sweep 45.2 (0.39) 4,659 4,665 45.2 (0.39) 4,709 4,712 # Marriages Age Biom., Avg. 0.81 (0.69) 7,779 7,780 0.89 (0.68) 7,516 7,516 # Cohabitations Age Biom., Avg. 0.68 (0.92) 7,779 7,780 0.66 (0.90) 7,516 7,516 # Months Unemployed (1978-2001) 8.6 (26.0) 7,780 7,780 4.3 (17.5) 7,516 7,516

The average age of the sample is 45, the average number of marriages by the time of the biomedical interview was 0.7 for men and 0.8 for women, and the average number of

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16 cohabitations 0.6. Finally, the average number of months spent unemployed between January 1978 and December 2001 was 7.4 among men and 3.9 among women. Fertility and Cardio-metabolic Risk Factors and Respiratory Volume In this section we present the results of the multivariate regressions for cardiovascular disease risk factors and respiratory function. All the tables and the graphs below report the coefficients of each fertility characteristic separately, and all the regressions include the control variables5. Number of Children Table 4 (and Figure 1) reports results for the association between parity and biomarkers. As we can see from the table the association is very weak. Both men and women with four or more children report a higher level of glycated haemoglobin, 0.134 and 0.129 respectively (significant at 1% level). The waist-hip ratio is slightly (i.e. 0.007) higher for women with four or more children (significant at 5% level). No other biomarkers show a significant association with parity. The lack of significance in the association between parity and most of the biomarkers is partly explained by all the control variables we included in the model. The relationship is strongly affected by early life socioeconomic background, childhood health and socioeconomic characteristics. The same models show a stronger association when we do not consider confounding variables (e.g. with fibrinogen, waist-hip ratio and expiratory volume; see Figure A1 in the appendix).

5 The tables in the text only show the coefficients/odds ratios for parity, age at first birth, and age at last birth.

See the Appendix for the tables with full results. Moreover, parity is not included as a control in the regressions for age at first and last birth, but as a robustness check we also ran the models using parity as a control and the results are very similar (available upon request).

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Table 4. Biomarkers and Number of Children log(Fibrinogen) log(C-Reactive Protein) Glycated Haemoglobin Cholesterol Ratio (Tot./HDL) Men Women Men Women Men Women Men Women Number of Children, Ref: 2 Children B/St Err B/St Err B/St Err B/St Err B/St Err B/St Err B/St Err B/St Err

0.009 0.013 0.046 0.060 0.001 0.022 0.000

0.023

(0.009) (0.009) (0.052) (0.057) (0.033) (0.032) (0.054) (0.049)

1

0.012 0.011 0.012 0.036

0.021

0.002

0.029
0.059

(0.009) (0.009) (0.050) (0.053) (0.032) (0.033) (0.048) (0.046)

3

0.004
0.008
0.021
0.021
0.015

0.006 0.014

0.002

(0.010) (0.009) (0.056) (0.049) (0.037) (0.031) (0.054) (0.048)

4+

0.008 0.006

0.002

0.010 0.134* 0.129* 0.038 0.065 (0.013) (0.012) (0.075) (0.072) (0.047) (0.037) (0.073) (0.063)

High Blood Pressure Obesity Waist-Hip Ratio Forced Expiratory Vol Men Women Men Women Men Women Men Women Number of Children, Ref: 2 Children OR/CI OR/CI OR/CI OR/CI B/St Err B/St Err B/St Err B/St Err

1.050 1.117 1.083 1.091 0.026

0.012
0.037

0.005 0.83,1.33 0.78,1.61 0.88,1.33 0.88,1.35 (0.024) (0.026) (0.031) (0.032)

1

1.007 1.007 0.945 0.993

0.010
0.006
0.003
0.002

0.80,1.27 0.70,1.46 0.78,1.14 0.81,1.22 (0.025) (0.025) (0.032) (0.030)

3

0.899 0.794 1.060 1.040

0.015

0.041

0.014

0.005 0.70,1.16 0.56,1.14 0.87,1.29 0.86,1.25 (0.025) (0.026) (0.034) (0.026)

4+

1.061 1.028 1.098 1.216

0.002

0.068**

0.030

0.010 0.76,1.49 0.64,1.64 0.84,1.44 0.94,1.57 (0.038) (0.034) (0.048) (0.036)

N

7,749 7,503 7,749 7,503 7,749 7,503 7,749 7,503

Note: * p<0.1, p<0.05, * p<0.01

SLIDE 18

18 Figure 1. Number of Children and Biomarkers Age at First Birth A different picture can be observed when we look at the age at first birth. As Table 5 and Figure 2 show, there is quite a strong association between the age at first childbirth and biomarkers for cardio-metabolic risk and respiratory function. Women having their first child after 30 report a lower level of fibrinogen. Levels of CRP are 13.5% higher for men having their first child when they are younger than 23. For women, CRP decreases monotonically the higher the age at first birth, with a 15.2% decrease for those having a child after age 35. Having the first child between age 30 and 34 for women contributes to a lower cholesterol ratio and lower risk of high blood pressure6. The age at first birth is also

6 As a robustness check we ran all the models including extra controls on the intake of medicines for

cardiovascular and respiratory systems, and the results are very similar.

1 3 4+ −.1 .1 .2

ln(fibrinogen)

1 3 4+ −.1 .1 .2

ln(CRP)

1 3 4+ −.1 .1 .2

Haemoglobin

1 3 4+ −.1 .1 .2

Chol. Ratio

1 3 4+ .5 1 1.5

High Blood Pressure, OR

1 3 4+ .5 1 1.5

Obesity, OR

1 3 4+ −.1 .1 .2

Waist−Hip Ratio(x10)

1 3 4+ −.1 .1 .2

FEV

Number of Children (Ref: 2 children)

Men Women

SLIDE 19

19 important for obesity among women, since having the first baby after 25 is associated with a lower probability of being obese than having a child before age 24. The waist-hip ratio is slightly higher for men having the first child before age 23 and lower for women having their first child after age 35. Finally, the expiratory volume is lower for men having a child before age 23 and women having a teenage pregnancy, while it is higher for women having their first child between 30 and 34. In general it seems that age at first birth is a more important predictor of health than parity (with which it is associated). In particular, there is an inverted U-shape (or better J-shape) relationship between age at first birth and health outcomes, with worse outcomes for very young ages (and sometimes also for ‘very’ old ages).

SLIDE 20

20

Table 5. Biomarkers and Age at 1st Birth log(Fibrinogen) log(C-Reactive Protein) Glycated Haemoglobin Cholesterol Ratio (Tot./HDL) Men Women Men Women Men Women Men Women Age at 1st Birth, Ref: 23-27 (M)/20-24 (W) B/St Err B/St Err B/St Err B/St Err B/St Err B/St Err B/St Err B/St Err <23(M)-<20(W) 0.006 0.009 0.135** 0.104

0.010

0.020 0.079 0.067 (0.011) (0.011) (0.067) (0.070) (0.041) (0.037) (0.063) (0.058) 28-32 (M) / 25-29 (W)

0.003
0.007
0.007
0.108*
0.058*
0.031
0.012
0.093*

(0.010) (0.009) (0.054) (0.055) (0.035) (0.029) (0.053) (0.049) 33-38 (M) / 30-34 (W)

0.016
0.023**

0.027

0.103
0.075*
0.047
0.064
0.198***

(0.011) (0.011) (0.064) (0.066) (0.040) (0.034) (0.062) (0.060) 39+ (M) / 35+ (W)

0.012
0.032**
0.038
0.152*
0.091
0.021

0.053

0.130

(0.018) (0.014) (0.106) (0.088) (0.066) (0.054) (0.100) (0.086) High Blood Pressure Obesity Waist-Hip Ratio10 Forced Expiratory Vol Men Women Men Women Men Women Men Women Age at 1st Birth, Ref: 23-27 (M)/20-24 (W) OR/CI OR/CI OR/CI OR/CI B/St Err B/St Err B/St Err B/St Err <23(M)-<20(W) 1.016 1.016 1.237 1.048 0.082*** 0.051

0.099** -0.104***

0.77,1.35 0.66,1.56 0.98,1.56 0.84,1.31 (0.031) (0.033) (0.039) (0.035) 28-32 (M) / 25-29 (W) 1.066 1.099 1.007 0.747*** 0.010

0.034
0.028

0.039 0.84,1.36 0.77,1.56 0.82,1.24 0.61,0.91 (0.027) (0.026) (0.035) (0.029) 33-38 (M) / 30-34 (W) 0.797 0.567 0.768 0.630***

0.038
0.039
0.029

0.064* 0.59,1.07 0.33,0.98 0.59,1.00 0.48,0.83 (0.032) (0.032) (0.040) (0.034) 39+ (M) / 35+ (W) 0.823 0.756 0.838 0.705*

0.047
0.097**

0.076 0.000 0.49,1.37 0.38,1.49 0.54,1.30 0.49,1.02 (0.052) (0.044) (0.069) (0.045) N 4,573 5,151 4,573 5,151 4,573 5,151 4,573 5,151 Note: * p<0.1, p<0.05, * p<0.01

SLIDE 21